Seasonal Activity Patterns of Bats in the Central …...around three caves in the central...

Seasonal Activity Patterns of Bats in

the Central Appalachians

Michael S. Muthersbaugh

Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in

partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

in

Fisheries and Wildlife

W. Mark Ford, Committee Chair

Alex Silvis

Karen E. Powers

February 20, 2017

Blacksburg, Virginia

Keywords: activity, bats, Central Appalachians, Virginia, caves, ridges, migration, atmospheric

conditions, White-nose Syndrome, roost trees

Abstract (academic)

Two threats to bats are especially pervasive in the central Appalachian Mountains of the eastern

United States: White-nose Syndrome (WNS) and wind energy development. These threats are

sufficient that multiple species are at risk of regional extirpation. White-nose Syndrome has

caused the death of millions of bats in North America, and multiple hibernating bat species are

affected in the central Appalachians (Indiana bat, Myotis sodalis; northern long-eared bat, Myotis

septentrionalis; little brown bat, Myotis lucifugus; eastern small-footed bat, Myotis leibii; eastern

tricolored bat, Perimyotis subflavus; big brown bat, Eptesicus fuscus). Population declines

attributed to WNS have led to the listing the northern long-eared bat as threatened under the

Endangered Species Act, and other affected bat species are likely to follow. Wind energy is one

of the most rapidly-growing energy sources in eastern United States, and primarily affects highly

migratory, non-hibernating bat species in the Appalachians (eastern red bat, Lasiurus borealis;

silver-haired bat, Lasionycteris noctivagans; hoary bat, Lasiurus cinereus). This anthropogenic

threat could cause the collapse of migratory bat populations, especially if operational mitigation

strategies are not implemented moving forward in the future especially as more wind energy

production facilities are installed. These threats represent great challenges for land managers to

reduce additional potential impacts to bats from other stressors such as other management

actions, i.e., forest management, prescribed fire, and urbanization/habitat conversion. Baseline

data is needed to help inform management decisions relative to bats. However, managers in the

Appalachians have limited data on general and species-specific activity and distributional

patterns, especially during the spring and autumn seasons; necessary information for formulating

suitable management strategies to benefit bat conservation. Furthermore, the effects of these

primary threats to populations of hibernating and migratory bat species may be exacerbated

during autumn and spring. Wind energy and WNS affect individual bat species differently, and

the extent of impacts can vary seasonally. Therefore, I sought to determine patterns and drivers

of activity for hibernating bat species during autumn and spring around hibernacula. Similarly, I

set out to determine patterns and drivers of activity for migratory bat species during autumn and

spring along ridgelines in the central Appalachians. I also explored data to search for potential

WNS-induced changes to summer ecology of northern long-eared bats, a species once common

in the central Appalachians. This study can help elucidate patterns of bat activity during largely

understudied seasons. Furthermore, it can provide useful information needed by land managers

to implement actions that could help alleviate and/or avoid potential additive negative impacts on

bat species with existing conservation concerns.

Abstract (public)

Two threats to bats are especially pervasive in the central Appalachian Mountains of the eastern

United States: a fungal disease called White-nose Syndrome (WNS) and wind energy

development. White-nose Syndrome has caused the death of millions of bats in North America,

and multiple hibernating bat species are affected in the central Appalachians. Wind energy is

one of the most rapidly-growing energy sources in eastern United States, and bats are often killed

when they fly near wind turbines. Fatality rates at wind turbines is highest in bat species that

migrate instead of hibernate. There is limited data on bats during the autumn and spring seasons

in the central Appalachian Mountains, and the impacts of WNS and wind energy development

may be exacerbated during these seasons. Therefore, I sought to determine patterns and drivers

of activity for hibernating bat species during autumn and spring around hibernacula. Similarly, I

set out to determine patterns and drivers of activity for migratory bat species during autumn and

spring along mountain ridgelines in the central Appalachians. Lastly, I searched for evidence of

potential WNS-induced changes in the summer ecology of the once common northern long eared

bat. This study can help elucidate patterns of bat activity during largely understudied seasons.

Furthermore, it can provide useful information needed by land managers to implement actions

that could help alleviate and/or avoid potential additive negative impacts on bat species with

existing conservation concerns.

iv

Acknowledgments

Funding was provided by the Joint Fire Science Program Grant # G14AC00316 and U.S.

Geological Survey Disease Program Grant #G15AC00487 through the U.S. Geological Survey

Cooperative Research Unit Program. I will always be indebted to my advisor, Mark Ford, for

affording me this opportunity. His feedback was nearly instantaneous, and he always had a backup

plan when things did not go as expected. I also must thank Alex Silvis for patiently teaching me how

to interpret complex ecological data. Karen Powers provided extremely helpful comments,

suggestions, and local study-site knowledge throughout the research process; beyond this, she helped

me understand how to properly use a semicolon and pay attention to detail in my writing. Marek

Smith and Laurel Schablein of The Nature Conservancy were invaluable guides for my work in Bath

County, VA. Bat capture help and extensive cave knowledge was provided by Rick Reynolds of the

Virginia Department of Game and Inland Fisheries. Justin Hall, Meggan Sellers, Keifer Titus, Sam

Hannabass, Amanda Rhyne, Katie Patrum, and Lauren Austin all provided essential field support.

v

Table of Contents

Abstract ............................................................................................................................................ i

Acknowledgments.......................................................................................................................... iv

Table of Contents ............................................................................................................................ v

List of Tables ................................................................................................................................ vii

List of Figures ............................................................................................................................... xv

Chapter 1: Activity patterns of cave-dwelling bat species during pre-hibernation swarming and

post-hibernation emergence in the central Appalachians ............................................................... 1

Abstract ............................................................................................................................... 1

Introduction ......................................................................................................................... 2

Methods............................................................................................................................... 6

Study Area ...................................................................................................................... 6

Data Collection ............................................................................................................... 6

Statistical Analyses ......................................................................................................... 8

Results ................................................................................................................................. 9

Discussion ......................................................................................................................... 16

Literature Cited ................................................................................................................. 23

Tables ................................................................................................................................ 29

Figures............................................................................................................................... 54

Chapter 2: Activity patterns in regional and long-distance migrant bat species during the fall and

spring along ridgelines in the central Appalachians. .................................................................... 78

Abstract ............................................................................................................................. 78

Introduction ....................................................................................................................... 79

Methods............................................................................................................................. 84

Study Area .................................................................................................................... 84

Data Collection ............................................................................................................. 85

Statistical Analyses ....................................................................................................... 87

Results ............................................................................................................................... 88

Discussion ......................................................................................................................... 92

Literature Cited ............................................................................................................... 100

Tables .............................................................................................................................. 107

Figures............................................................................................................................. 116

Chapter 3: Impacts of White-nose Syndrome on maternity colony day roost selection by Myotis

septentrionalis in the central Appalachians ................................................................................ 144

vi

Abstract ........................................................................................................................... 144

Introduction ..................................................................................................................... 145

Methods........................................................................................................................... 148

Study Area .................................................................................................................. 148

Data Collection ........................................................................................................... 149

Statistical Analyses ..................................................................................................... 151

Results ............................................................................................................................. 151

Discussion ....................................................................................................................... 153

Literature Cited ............................................................................................................... 157

Tables .............................................................................................................................. 162

Figures............................................................................................................................. 166

vii

List of Tables

Table 1-1: Variables used in candidate models representing hypotheses regarding bat activity

around three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015

and 2016, and spring 2016 and 2017. Variables were used in different combinations, and highly

correlated variables were not included within a single candidate model. ..................................... 29

Table 1-2: Variables used in candidate models describing bat activity with justification and

supporting literature for each parameter. Candidate models represented hypotheses regarding bat

activity around three caves in the central Appalachians, Virginia and West Virginia, during

autumn 2015 and 2016, and spring 2016 and 2017. ..................................................................... 30

Table 1-3: Estimates and 95% confidence intervals (CI) from the best supported model

predicting Myotis species’ (Myotis leibii, eastern small-footed bat; Myotis lucifugus, little brown

bat; Myotis septentrionalis, northern long-eared bat; Myotis sodalis, Indiana bat) activity around

three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and

2016. An asterisk (*) between predictors indicates an interaction. .............................................. 32

Table 1-4: Estimates and 95% confidence intervals (CI) from the best supported post hoc model






predicting ‘cavebat’ species’ (Eptesicus fuscus, big brown bat; Myotis leibii, eastern small-footed

bat; Myotis lucifugus, little brown bat; Myotis septentrionalis, northern long-eared bat; Myotis

sodalis, Indiana bat; Perimyotis subflavus, eastern tricolored bat) activity around three caves in

viii

the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. An asterisk

(*) between predictors indicates an interaction. ........................................................................... 34







Table 1-7: Rankings of models predicting Eptesicus fuscus (big brown bat) activity around three

caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016,

with k (number of parameters), Akaike’s information criteria (AIC) value, Akaike’s information

criteria (AICc) value corrected for small sample size, difference in AICc value between best

supported model and ith model (ΔAICc), wi (model weight), and ERi (evidence ratio). .............. 36


predicting Eptesicus fuscus (big brown bat) activity around three caves in the central

Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. ................................ 37



Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. An asterisk (*)

between predictors indicates an interaction. ................................................................................. 38

Table 1-10: Rankings of models predicting Myotis septentrionalis (northern long-eared bat)


autumn 2015 and 2016, with k (number of parameters), Akaike’s information criteria (AIC)

ix

value, Akaike’s information criteria (AICc) value corrected for small sample size, difference in

AICc value between best supported model and ith model (ΔAICc), wi (model weight), and ERi

(evidence ratio). ............................................................................................................................ 39


predicting Myotis septentrionalis (northern long-eared bat) activity around three caves in the

central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. .................... 40



central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. An asterisk (*)



predicting Myotis sodalis (Indiana bat) activity around three caves in the central Appalachians,

Virginia and West Virginia, during autumn 2015 and 2016. An asterisk (*) between predictors

indicates an interaction. ................................................................................................................ 42








three caves in the central Appalachians, Virginia and West Virginia, during spring 2016 and


x










the central Appalachians, Virginia and West Virginia, during spring 2016 and 2017. An asterisk










Appalachians, Virginia and West Virginia, during spring 2016 and 2017. An asterisk (*) between

predictors indicates an interaction. ............................................................................................... 48



xi


predictors indicates an interaction. ............................................................................................... 49



central Appalachians, Virginia and West Virginia, during spring 2016 and 2017. ...................... 50



central Appalachians, Virginia and West Virginia, during spring 2016 and 2017. An asterisk (*)




Virginia and West Virginia, during spring 2016 and 2017. An asterisk (*) between predictors






Table 2-1: Variables used in candidate models representing hypotheses regarding migratory bat

activity along five ridgelines and adjacent sideslopes in the central Appalachians, Virginia,

during autumn 2015 and 2016, and spring 2016 and 2017. Variables were used in different

combinations, and highly correlated variables were not included within a single candidate model.

..................................................................................................................................................... 107

xii




during autumn 2015 and 2016, and spring 2016 and 2017. ........................................................ 108

Table 2-3: Relationship between hourly activity of migratory bats (silver-haired bat-

Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) and

regional hourly atmospheric conditions along 5 ridgelines and adjacent sideslopes in the central

Appalachians, Virginia, during autumn 2015 and 2016. ............................................................ 109

Table 2-4: Relationship between hourly activity of silver-haired bats (Lasionycteris noctivagans)

and regional hourly atmospheric conditions along 5 ridgelines and adjacent sideslopes in the

central Appalachians, Virginia, during autumn 2015 and 2016. ................................................ 110

Table 2-5: Relationship between hourly activity of eastern red bats (Lasiurus borealis) and


Appalachians, Virginia, during autumn 2015 and 2016. ............................................................ 111




Appalachians, Virginia, during spring 2016 and 2017. .............................................................. 112



central Appalachians, Virginia, during spring 2016 and 2017. .................................................. 113

xiii

Table 2-8: Relationship between hourly activity of eastern red bat (Lasiurus borealis) and



Table 2-9: Relationship between hourly activity of hoary bats (Lasiurus cinereus) and regional

hourly atmospheric conditions along 5 ridgelines and adjacent sideslopes in the central


Table 3-1: Female northern long-eared bat (Myotis septentrionalis) day roosts by tree species,

number found, percentage of total day roosts, roost days (number of days used) by radio-tagged

bats, and percentage of total roost days by radio-tagged bats in deciduous hardwood forest on

George Washington and Jefferson National Forest and adjacent private lands in Bath County,

Virginia, 2015 and 2016. ............................................................................................................ 162

Table 3-2: Tree species selected as day-roosts and % total uses by female northern long-eared bat

(Myotis septentrionalis) maternity colonies on George Washington and Jefferson National Forest

and adjacent private lands in Bath County, Virginia, 2015 and 2016 (following White-nose

Syndrome, Post-WNS); randomly selected trees within landscape surrounding Post-WNS day-

roosts; and tree species selected as day-roosts and % total uses by female northern long-eared bat

(Myotis septentrionalis) maternity colonies on the Fernow Experimental Forest, Tucker County,

Virginia, 2007 and 2008 (prior to White-nose Syndrome, Pre-WNS). ...................................... 163

Table 3-3: Mean ± SD values of female northern long-eared bat (Myotis septentrionalis) day

roost characteristics and percent of trees in each crown class for Post-WNS day roost trees

located on George Washington and Jefferson Forest and private lands (Bath County, VA) and

randomly selected trees located within and on the surrounding landscape. An asterisk (*)

xiv

indicates a significant difference (P < 0.05) between characteristics of day roosts located post-

WNS and those of randomly selected trees. ............................................................................... 164

Table 3-4: Mean ± SD values of female northern long-eared bat (Myotis septentrionalis) day

roost characteristics and percent of trees in each crown class for pre-White-nose Syndrome

(WNS) day roost trees located on the Fernow Experimental Forest (Tucker County, West

Virginia) and Post-WNS day roost trees located on George Washington and Jefferson Forest and

private lands (Bath County, Virginia). An asterisk (*) indicates a significant difference (P < 0.05)

between characteristics of day roosts located post-WNS and those of day roosts located pre-

WNS.. .......................................................................................................................................... 165

xv

List of Figures

Figure 1-1: Approximate locations of caves where acoustic sampling was conducted for bat

species during autumn 2015 and 2016 and spring 2016 and 2017; two are located in the Ridge

and Valley physiographic sub-province the central Appalachians of Virginia (A and B), and the

third is in the unglaciated central Appalachian Plateau physiographic sub-province of West

Virginia (C). .................................................................................................................................. 54

Figure 1-2: Example of acoustic sampling setup around cave sites. Acoustic detector locations

were chosen based on land ownership, access, and proximity to the ‘km rings’, in habitats where

Myotis sodalis (Indiana bat) and Myotis septentrionalis (northern long-eared bat) presence was

likely, and where habitat physical features supported high-quality recordings. Acoustic detectors

were deployed in this manner around three caves in the central Appalachians, Virginia and West

Virginia, during autumn 2015 and 2016 and spring 2016 and 2017. ........................................... 55

Figure 1-3: Partial effects plot of the interacting relationship between mean daily temperatures,

date, and Myotis species’ (Myotis leibii, eastern small-footed bat; Myotis lucifugus, little brown

bat; Myotis septentrionalis, northern long-eared bat; Myotis sodalis, Indiana bat) echolocation

passes per night (with 95% confidence intervals) around three caves in the central Appalachians,

Virginia and West Virginia, during autumn 2015 and 2016. Panels show differences in number

of passes between sampling years. Predicted activity from an early- (blue), mid-(black), and late-

season (red) date are shown. ......................................................................................................... 56

Figure 1-4: Partial effects plot of the interacting relationship between mean daily temperatures,

date and ‘cavebat’ species’ (Eptesicus fuscus, big brown bat; Myotis leibii, eastern small-footed


sodalis, Indiana bat; Perimyotis subflavus, eastern tricolored bat) echolocation passes per night

xvi

(with 95% confidence intervals) around three caves in the central Appalachians, Virginia and

West Virginia, during autumn 2015 and 2016. Panels show differences in number of passes

between sampling years. Predicted activity from an early- (blue), mid-(black), and late-season

(red) date are shown. ..................................................................................................................... 57

Figure 1-5: Partial effects plot of the relationship between maximum daily temperature and

Eptesicus fuscus, big brown bat (EPFU), echolocation passes per night (with 95% confidence

intervals) around three caves in the central Appalachians, Virginia and West Virginia, during

autumn 2015 and 2016. ................................................................................................................. 58

Figure 6: Partial effects plot of the interacting relationship between date, distance to hibernacula

and Eptesicus fuscus, big brown bat (EPFU), echolocation passes per night around three caves in

the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. Confidence

intervals not shown for clarity. ..................................................................................................... 59

Figure 1-7: Partial effects plot of the relationship between maximum daily temperatures, date and

northern long-eared bat, Myotis septentrionalis, northern long-eared bat (MYSE), echolocation

passes per night (with 95% confidence intervals) around three caves in the central Appalachians,

Virginia and West Virginia, during autumn 2015 and 2016. Panels show differences in number

of passes between sampling years. Predicted activity from an early- (blue), mid-(black), and late-

season (red) date are shown. ......................................................................................................... 60

Figure 1-8: Partial effects plot of the relationship between date, distance to hibernacula, and

Myotis septentrionalis, northern long-eared bat (MYSE), echolocation passes per night (with

95% confidence intervals) around three caves in the central Appalachians, Virginia and West

Virginia, during autumn 2015 and 2016. ...................................................................................... 61

xvii

Figure 1-9: Partial effects plot of the interacting relationship between maximum daily

temperatures, date, and Myotis sodalis, Indiana bat (MYSO), echolocation passes per night (with


Virginia, during autumn 2015 and 2016. Panels show differences in number of passes between

sampling years. Predicted activity from an early- (blue), mid-(black), and late-season (red) date

are shown. ..................................................................................................................................... 62

Figure 1-10: Partial effects plot of the relationship between date, distance to hibernacula and

Myotis sodalis, Indiana bat (MYSO), echolocation passes per night around three caves in the

central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. Confidence

intervals not shown for clarity. ..................................................................................................... 63

Figure 1-11: Partial effects plot of the relationship between mean daily temperatures, date, and

Myotis species’ (Myotis leibii, eastern small-footed bat; Myotis lucifugus, little brown bat; Myotis

septentrionalis, northern long-eared bat; Myotis sodalis, Indiana bat) echolocation passes per

night (with 95% confidence intervals) around three caves in the central Appalachians, Virginia

and West Virginia, during spring 2016 and 2017. Predicted activity from an early- (blue), mid-

(black), and late-season (red) date are shown. .............................................................................. 64

Figure 1-12: Partial effects plot of the relationship between maximum daily temperatures,

proximity to caves, and ‘cavebat’ species’ (Eptesicus fuscus, big brown bat; Myotis leibii, eastern

small-footed bat; Myotis lucifugus, little brown bat; Myotis septentrionalis, northern long-eared

bat; Myotis sodalis, Indiana bat; Perimyotis subflavus, eastern tricolored bat) echolocation passes

per night (with 95% confidence intervals) around three caves in the central Appalachians,

Virginia and West Virginia, during spring 2016 and 2017. Predicted activity from sample sites

xviii

proximal to cave entrances (blue), and distal sites up to 3 km away from cave entrances (black)

are shown. ..................................................................................................................................... 65

Figure 1-13: Partial effects plot of the relationship between date, distance to cave, and ‘cavebat’

species’ (Eptesicus fuscus, big brown bat; Myotis leibii, eastern small-footed bat; Myotis

lucifugus, little brown bat; Myotis septentrionalis, northern long-eared bat; Myotis sodalis,

Indiana bat; Perimyotis subflavus, eastern tricolored bat) echolocation passes per night around


2017............................................................................................................................................... 66

Figure 1-14: Partial effects plots of the relationship between maximum daily temperatures and

Eptesicus fuscus, big brown bat (EPFU), echolocation passes per night (with 95% confidence


spring 2016 and 2017. ................................................................................................................... 67

Figure 1-15: Partial effects plot of the interacting relationship between date, distance to cave, and

Eptesicus fuscus, big brown bat (EPFU), echolocation passes per night around three caves in the

central Appalachians, Virginia and West Virginia, during spring 2016 and 2017. Confidence

intervals not shown for clarity. The EPFU relative activity increased at distal sites faster than at

cave sites later in the spring. ......................................................................................................... 68

Figure 1-16: Partial effects plot of the relationship between minimum daily temperatures,

proximity to cave, and Myotis septentrionalis, northern long-eared bat (MYSE), echolocation

passes per night (with 95% confidence interval) around three caves in the central Appalachians,

Virginia and West Virginia, during spring 2016 and 2017. Predicted activity from sample sites

proximal to cave entrances (blue), and distal sites up to 3 km away from cave entrances (black)

are shown. ..................................................................................................................................... 69

xix

Figure 1-17: Partial effects plot of the interactive relationship between date, distance to cave, and

Myotis septentrionalis, northern long-eared bat (MYSE), echolocation passes per night (with


Virginia, during spring 2016 and 2017. ........................................................................................ 70

Figure 1-18: Partial effects plot of the relationship between mean daily temperature, date, and

Myotis sodalis, Indiana bat (MYSO), echolocation passes per night (with 95% confidence


spring 2016 and 2017. Predicted activity from an early- (blue), mid-(black), and late-season (red)

date are shown............................................................................................................................... 71

Figure 1-19: Partial effects plot of the relationship between mean daily wind speed, proximity to

cave, and Myotis sodalis, Indiana bat (MYSO), echolocation passes per night (with 95%

confidence intervals) around three caves in the central Appalachians, Virginia and West Virginia,

during spring 2016 and 2017. Predicted activity from sample sites proximal to cave entrances

(blue), and distal sites up to 3 km away from cave entrances (black) are shown. ........................ 72

Figure 1-20: Smoothed trend line of raw data showing general timing of Myotis species’ (Myotis

leibii, eastern small-footed bat; Myotis lucifugus, little brown bat; Myotis septentrionalis,

northern long-eared bat; Myotis sodalis, Indiana bat) echolocation passes per night around three

caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. 73

Figure 1-21: Smoothed trend line of raw data showing general timing of ‘cavebat’ species’

(Eptesicus fuscus, big brown bat; Myotis leibii, eastern small-footed bat; Myotis lucifugus, little

brown bat; Myotis septentrionalis, northern long-eared bat; Myotis sodalis, Indiana bat;

Perimyotis subflavus, eastern tricolored bat) echolocation passes per night around three caves in

the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. .............. 74

xx

Figure 1-22: Smoothed trend line of raw data showing general timing of Eptesicus fuscus, big

brown bat (EPFU), echolocation passes per night around three caves in the central Appalachians,

Virginia and West Virginia, during autumn 2015 and 2016. Confidence intervals not shown for

clarity. ........................................................................................................................................... 75

Figure 1-23: Smoothed trend line of raw data showing general timing of Myotis septentrionalis,

northern long-eared bat (MYSE), echolocation passes per night around three caves in the central

Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. ................................ 76

Figure 1-24: Smoothed trend line of raw data showing general timing of Myotis sodalis, Indiana

bat (MYSO), echolocation passes per night around three caves in the central Appalachians,

Virginia and West Virginia, during autumn 2015 and 2016. ........................................................ 77

Figure 2-1: Approximate locations of five ridges sampled in central Appalachians of Virginia.

The ridges sampled in Giles and Bath counties occur in the Ridge and Valley sub-physiographic

province, whereas the ridges sampled in Rockingham and Madison counties occur in the Blue

Ridge sub-physiographic province. Acoustic sampling occurred during autumn 2015 and 2016

and spring 2016 and 2017. .......................................................................................................... 116

Figure 2-2: Example of acoustic sampling survey sites on ridgelines and adjacent sideslopes.

Acoustic detectors were deployed in this manner along five ridgelines in the central

Appalachians, Virginia, during autumn 2015 and 2016 and spring 2016 and 2017. .................. 117

Figure 2-3: Partial effects plot of the relationship between date and migratory bat species (silver-

haired bat-Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus

cinereus) echolocation passes per hour (with 95% confidence intervals shade gray) along five

ridgelines in the central Appalachians, Virginia, during autumn 2015 and 2016. ...................... 118

xxi

Figure 2-4: Partial effects plot of the relationship between mean hourly temperature (Celsius)

and migratory bat species (silver-haired bat-Lasionycteris noctivagans, eastern red bat-Lasiurus

borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95% confidence

intervals) along five ridgelines in the central Appalachians, Virginia, during autumn 2015 and

2016............................................................................................................................................. 119

Figure 2-5: Partial effects plot of the relationship between hour of night, relative elevation, and

migratory bat species (silver-haired bat-Lasionycteris noctivagans, eastern red bat-Lasiurus


intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during autumn

2015 and 2016. ............................................................................................................................ 120

Figure 2-6: Partial effects plot of the relationship between hourly barometric pressure and

migratory bat species (silver-haired bat-Lasiurus noctivagans, eastern red bat-Lasiurus borealis,

hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95% confidence intervals shade

gray) along five ridgelines in the central Appalachians, Virginia, during autumn 2015 and 2016.

..................................................................................................................................................... 121

Figure 2-7: Partial effects plot of the relationship between change in ambient temperature from 4

hours prior and migratory bat species (silver-haired bat-Lasionycteris noctivagans, eastern red

bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95%

confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia,

during autumn 2015 and 2016. Activity was greater when the drop in temperature from 4 hours

prior was greater. ........................................................................................................................ 122

Figure 2-8: Partial effects plot of the relationship between date and silver-haired bat

(Lasionycteris noctivagans) echolocation passes per hour (with 95% confidence intervals shade

xxii


..................................................................................................................................................... 123

Figure 2-9: Partial effects plot of the relationship between hour of sampling each night and

silver-haired bat (Lasionycteris noctivagans) echolocation passes per hour (with 95% confidence

intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during autumn

2015 and 2016. ............................................................................................................................ 124

Figure 2-10: Partial effects plot of the relationship between mean hourly ambient temperature

(Celsius) and silver-haired bat (Lasionycteris noctivagans) echolocation passes per hour (with

95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia,

during autumn 2015 and 2016. ................................................................................................... 125

Figure 2-11: Partial effects plot of the relationship between change in ambient temperature from

4 hours prior and silver-haired bats (Lasionycteris noctivagans) echolocation passes per hour

(with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians,

Virginia, during autumn 2015 and 2016. Activity was greater when the drop in temperature from

4 hours prior was greater............................................................................................................. 126

Figure 2-12: Partial effects plot of relationship between hour of sampling each night and eastern

red bat (Lasiurus borealis) echolocation passes per hour (with 95% confidence intervals shade


..................................................................................................................................................... 127

Figure 2-13: Partial effects plot of the relationship between mean hourly wind speed (KPH) and

eastern red bat (Lasiurus borealis) echolocation passes per hour (with 95% confidence intervals

shade gray) along five ridgelines in the central Appalachians, Virginia, during autumn 2015 and

2016............................................................................................................................................. 128

xxiii

Figure 2-14: Partial effects plot of the relationship between date and eastern migratory bat

species (silver-haired bat-Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary

bat-Lasiurus cinereus) echolocation passes per hour (with 95% confidence intervals shade gray)

along five ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017. ...... 129

Figure 2-15: Partial effects plot of the relationship between sampling hour of night and migratory

bat species (silver-haired bat-Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary

bat-Lasiurus cinereus) echolocation passes per hour (with 95% confidence intervals shade gray)


Figure 2-16: Partial effects plot of the relationship between ambient hourly temperature (Celsius)

and migratory bat species (silver-haired bat-Lasionycteris noctivagans, eastern red bat-Lasiurus


intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during spring

2016 and 2017. ............................................................................................................................ 131

Figure 2-17: Partial effects plot of the relationship between date and silver-haired bat

(Lasionycteris noctivagans) echolocation passes per hour (with 95% confidence intervals shade

gray) along five ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017.

..................................................................................................................................................... 132

Figure 2-18: Partial effects plot of the relationship between sampling hour of night and silver-

haired bat (Lasionycteris noctivagans) echolocation passes per hour (with 95% confidence


2016 and 2017. ............................................................................................................................ 133

Figure 2-19: Partial effects plot of the relationship between hourly ambient temperature (Celsius)

and silver-haired bat (Lasionycteris noctivagans) echolocation passes per hour (with 95%

xxiv

confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia,

during spring 2016 and 2017. ..................................................................................................... 134

Figure 2-20: Partial effects plot of the relationship between hourly wind speed (KPH) and silver-

haired bat (Lasionycteris noctivagans) echolocation passes per hour (with 95% confidence


2015 and 2016. ............................................................................................................................ 135

Figure 2-21: Partial effects plot of the relationship between date and eastern red bat (Lasiurus

borealis) echolocation passes per hour (with 95% confidence intervals shade gray) along five

ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017......................... 136

Figure 2-22: Partial effects plot of the relationship between sampling hour of night and eastern



..................................................................................................................................................... 137


and eastern red bat (Lasiurus borealis) echolocation passes per hour (with 95% confidence


2016 and 2017. ............................................................................................................................ 138

Figure 2-24: Partial effects plot of the relationship between hourly wind speed (KPH) and eastern



..................................................................................................................................................... 139

xxv

Figure 2-25: Partial effects plot of the relationship between date and hoary bat (Lasiurus

cinereus) echolocation passes per hour (with 95% confidence intervals shade gray) along five

ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017......................... 140

Figure 2-26: Partial effects plot of the relationship between sampling hour of night and hoary bat

(Lasiurus cinereus) echolocation passes per hour (with 95% confidence intervals shade gray)



and hoary bat (Lasiurus cinereus) echolocation passes per hour (with 95% confidence intervals

shade gray) along five ridgelines in the central Appalachians, Virginia, during spring 2016 and

2017............................................................................................................................................. 142

Figure 2-28: Photograph of an eastern red bat (Lasiurus borealis) roosting in litter (left), and

approximate location of roost on hillside (right) at Pandapas Pond Recreational area, George

Washington and Jefferson National Forest, Montgomery County, VA, on 03/08/2015.

Photograph credit: Andrew Kniowski. ....................................................................................... 143

Figure 3-1: Locations of female northern long-eared bat (Myotis septentrionalis) day roosts

found in Bath County, VA, during summer 2015 and 2016. The minimum convex polygon

(MCP) for 2015 roosts encompassed 20.8 hectares, and the MCP for the 2016 roosts

encompassed 39.8 hectares. ........................................................................................................ 166

1

Chapter 1: Activity patterns of cave-dwelling bat species during pre-hibernation swarming and

post-hibernation emergence in the central Appalachians

Abstract

Bat conservation and research efforts largely have focused on summer maternity colonies and

winter hibernacula, leaving immediately pre- and post-hibernation ecology for most species

unclear. Understanding these topics is critical for addressing potential additive impacts to White-

nose Syndrome (WNS)-affected bats, especially during staging for hibernation and migration,

respectively. To examine fall and spring bat activity patterns in the Appalachian Mountains of

Virginia and West Virginia, we acoustically monitored bat activity around three hibernacula

from early September through mid-November 2015 and 2016, and from early March through

April 2016 and 2017. We assessed the effects of distance to hibernacula and ambient conditions

on nightly bat activity using generalized linear mixed effects models. Overall bat activity was

extremely limited at all sample sites through both the fall and spring sample periods except at

sites proximal to hibernacula entrances. Best-supported models describing bat activity varied

amongst individual bat species and species-groups, but date and ambient temperatures generally

appeared to be major drivers of activity in autumn and spring. Overall, autumn bat activity

around hibernacula was variable through the sampling period, but total activity for all species

had largely ceased by mid-November. Spring bat activity also varied through the sampling

period, and bats were active by mid-March.

2

Introduction

Prior to hibernation, many temperate biome cave bat species “swarm” around hibernacula

to mate and find suitable hibernation sites (Davis and Hitchcock 1965, Barclay et al. 1979,

Schaik et al. 2015). This time also is vital for weight gain and fat deposition, a necessity to

survive the energy demands of hibernation (Ewing et al. 1970, Kunz et al. 1998, Jonasson and

Willis 2011, Reeder et al. 2012). White-nose Syndrome (WNS) has caused millions of bats to die

during hibernation, largely due to changes in behavior that deplete energy reserves (Blehert et al.

2009, Frick et al. 2016). During the late summer and early fall, hundreds of individual bats may

engage in swarming behavior outside a hibernaculum (Rivers et al. 2005, Schaik et al. 2015),

with males being most active (in flight more hours each night) and for a longer period into the

autumn at or near hibernacula (Schowalter 1980, Lowe 2012, Burns and Broders 2015). Prior to

entering hibernation in mid- to late-fall, bats continue to roost on the landscape, in trees, rocks,

and human structures (Brack 2006, Lowe 2012). Effects of ambient conditions on bat activity,

regardless of sex, during the fall swarming period, and specifically around known hibernacula,

are understood poorly. A greater examination of how ambient conditions affect the timing of fall

swarm activity and subsequent hibernation phenology could help managers actively define and

manage critical autumn swarm habitat. These insight may help prevent accidental ‘take’, as

defined under Section 3 of the Endangered Species Act (ESA 1973, as amended; U.S. Office of

the Federal Register 2015), especially with regard to the federally-endangered Indiana bat

(Myotis sodalis; hereafter MYSO).

During the spring emergence/staging period, cave hibernating bats emerge from caves

and disperse across the landscape, with reproductively-active females establishing maternity

3

colonies (Caire et al. 1979, Whitaker and Hamilton 1998). Emerging bats typically do not re-

enter hibernacula, instead roost across the landscape. However, observations of spring roost sites

are limited compared to well-studied summer roosts (Britzke et al. 2006). Foraging and prey

availability around hibernacula during the spring, when energy demands are high post-

hibernation, are important factors affecting movement to maternity areas and subsequent

reproductive success (Frick et al. 2016, Meyer et al. 2016). Though spring emergence may not be

triggered by prey availability per se, and is probably linked directly to temperature and pressure

changes, photoperiod, and circannual rhythms. Meyer et al. (2016) suggested that female bats’

arrival at colony areas coincides with increasing insect abundance, even if prey availability was

low at first emergence.

Migration to maternity areas consumes valuable energy stores, and WNS-related

impairments could exacerbate energy losses in impacted species (Britzke et al. 2006, Frick et al.

2016). There appear to exist considerable differences in spring ecology between populations and

geographic regions. For example, female MYSO in the midwestern United States may travel as

far as 100-500 km to reach summer maternity areas, whereas female MYSO from hibernacula in

New York travelled only 27 km, on average (Kurta and Murray 2002, Britzke et al. 2006, Pettit

and O’Keefe 2017). Data about spring movements are lacking for northern long-eared bats

(Myotis septentrionalis; hereafter MYSE), and while not typically considered a ‘migratory’ bat

species, they can travel up to 160 km to summer foraging/maternity colony areas (USFWS,

2014). Because these migrations pose great energy demands, foraging may be an important

activity immediately post-emergence. Foraging success, however, is tied closely to prey

availability, which in turn is affected by weather conditions (Williams 1940, Meyer et al. 2016).

Spatio-temporal assessments of bat activity post-emergence will allow managers to recognize

4

vital habitat and time periods, thus encouraging management that provides maximum

conservation benefit to dwindling bat populations in the central Appalachians. Accordingly, the

dynamics of bat activity during autumn swarm and spring staging seasons, both immediately

around hibernacula and elsewhere are critical data gaps for post-WNS species management.

Critical to understanding the effects of WNS on emergence timing and dynamics (Blehert

et al. 2009, Frick et al. 2016) is the influence on reproductive output (Czenze and Willis 2015).

Female bats emerge earlier than males regardless of WNS-related impacts (Norquay and Willis

2014, Czenze and Willis 2015), and WNS causes bats (regardless of sex) to emerge earlier in the

spring (Blehert et al. 2009, Norquay and Willis 2014), which may prove detrimental to species

recruitment. Clearly, WNS compromises female bats’ physiologic conditions during hibernation

(Frick et al. 2016). To this effect, Francl et al. (2012) proposed that female bats may abort

pregnancy if crucial energy sources are too limited to provide ample energy required for fetal or

juvenile development.

In other mammalian taxa (e.g., genus Marmota), reproductive success is greater with

earlier emergence from hibernation in favorable conditions (Ozgul et al. 2010), but this remains

speculative for bats (Czenze and Willis 2015). Unlike larger mammals, bats may not be able to

withstand the danger of a cold snap in the early spring. Furthermore, WNS increases overwinter

energy consumption, and reproductive failure may negate any physiological benefits of emerging

earlier. Females lacking the physiological energy constraints of pregnancy may have fewer

behavioral/roosting constraints, thus could conceivably emerge from hibernation very early with

less deleterious effects, though this remains speculative. Non-reproductive females may have a

better chance of surviving through the spring and summer.

5

Under the Indiana Bat Protection and Enhancement Plan Guidelines (USFWS 2009),

hibernacula where MYSO exist are afforded protective buffer zones, where tree clearing

activities are seasonally restricted. Within an 8 km radius of Priority 3 and Priority 4 MYSO

hibernacula (USFWS 2007), tree clearing must occur between November 15 and March 31 to

avoid disturbing MYSO day-roosting in trees (USFWS 2009). These guidelines aim to minimize

adverse effects on Indiana bats during the fall swarm and spring emergence periods when they

use forested habitats surrounding hibernacula.

A regional understanding of acoustic bat activity patterns around hibernacula during

spring and autumn will help determine the sufficiency of these protective cave buffers, especially

for the federally-listed species such as MYSE and MYSO whose populations have declined

substantially due to WNS. Broadly, acoustic bat activity can be defined as a count of

echolocation pass files (hereafter activity). In Virginia, Powers et al. (2015) found evidence of

massive WNS-induced population declines across multiple bat species; yet whether population

declines and physiologic changes affect spring and autumn activity remains unclear.

My objectives were to determine major activity patterns and define drivers of activity of

cave-hibernating bats during the pre-hibernation staging period and the spring emergence period

around caves in the central Appalachians after the onset of WNS. I hypothesized that activity

would greatly vary both temporally and spatially around caves, but activity would occur

throughout the sampling season, specifically proximal to cave entrances (Whitaker and Rissler

1992, Bernard and McCracken 2017). Furthermore, I hypothesized that ambient conditions act as

indicators and cause bats to restrict or increase activity during autumn and spring in habitats

around caves.

6

Methods

Study Area

I conducted my study around two caves in the Virginia Ridge and Valley sub-province of

the Appalachian Mountains and one cave in the West Virginia unglaciated Appalachian Plateau

sub-province (Figure 1-1). These three caves were chosen because of similar numbers of

hibernating Indiana bats pre-WNS along with multi-year cave-count records (Powers et al.

2015), and a south to north clinal arrangement pattern on the regional landscapes. Each of the

three caves sampled are considered Priority 3 hibernacula (based on cave count data, USFWS

2007). The two Virginia caves I studied are located in Bath and Bland counties (hereafter Caves

A and B, respectively), and the West Virginia cave is located in Tucker County (hereafter Cave

C; specific names withheld to protect locations of federally-listed species’ hibernacula; U.S.

FCRPA 1988). The forests surrounding Caves A and B are generally xeric-to moderately mesic

oak (Quercus spp.) associations on ridges and other areas with well-drained soils, and mixed

mesophytic forest along riparian areas and north-facing aspects (Braun 1950). The predominant

forest surrounding Cave C is a mixed mesophytic/Allegheny hardwood association (Ford et al.

2005). The landscape immediately surrounding both Cave A and Cave C is primarily forested,

whereas the landscape surrounding Cave B is a matrix of forest and agricultural land.

Data Collection

In the autumn, I monitored bat activity at the main entrance of each cave and at one km,

two km, and three km distances from cave entrances (Figure 1-2) from early September to mid-

November in 2015 and 2016, when cave bats swarm and mate around hibernacula. For the

spring, I monitored bat activity in early March to late April in 2016 and 2017, when bats are

leaving hibernacula and dispersing across the landscape for the summer months (Whitaker and

7

Rissler 1992, Caceres and Barclay 2000). For each cave, only one detector was placed near the

cave entrance (away from acoustically reflective surface), and two were deployed at each radii

distance. Detector locations were chosen based on accessibility (landowner permission and

topography), likelihood of MYSE and MYSO presence (Ford et al. 2005), and site characteristics

known to produce high-quality call recordings (i.e., low clutter such as a forest canopy

gap/riparian corridor). Detectors at the same radii were spaced > 100 m apart to ensure that

individual bats were not sampled on two detectors simultaneously and to maintain quasi-

independent sampling units (Ford et al. 2005). I recorded acoustic data using Song Meter ZC

detectors (SMM-U1 microphones), Song Meter SM2 (SMX-U1 microphones), and Song Meter

SM4 detectors (SMM-U1 microphones, Wildlife Acoustics, Maynard, Massachusetts). I

programmed detectors to record nightly from 1900 to 0700 hours. I collected local weather data

in Meteorological Terminal Aviation Routine (METAR) format from digital records, from the

airport nearest to each detector site (https://www.wunderground.com/, 2017).

I identified acoustic call data to species using the United States Fish and Wildlife Service

approved Kaleidoscope version 4.3.1 (Wildlife Acoustics, Maynard, Massachusetts) classifier

4.2.0 at the neutral setting, with default signal parameters (8-120 KHz frequency range, 500

maximum inter-syllable gap, minimum of two pulses, enhance with advanced signal processing

(USFWS 2017a). I manually checked recorded files using program Analook (Titley Electronics,

Columbia, Missouri) to ensure there were no major misclassification errors (e.g., noise files

consistently classified as bat echolocation pass). I was not concerned with overall accuracy of the

program to assess levels of activity, as all bat species examined were known to be present at my

sites, therefore I assumed constant bias in automated file identification (Ford 2017).

8

Statistical Analyses

I created a set of a priori candidate models representing specific hypotheses about the

relationship between habitat variables and bat activity, using the variables and models for

analyses of both fall and spring data. Candidate models included combinations of date, site,

landscape characteristics, and ambient conditions (Table 1-1, Table 1-2). I assessed

multicollinearity among predictors to ensure highly correlated variables were not included within

the same model using package corrplot (Wei and Simko 2016) in program R version 3.2.3 (R

Core Team 2013). I tested for autocorrelation in daily bat activity, for each species using R

package nlme (Pinheiro et al. 2017). I modelled nightly acoustic bat activity using negative

binomial mixed models (GLMM), with nested random effects to account for the correlated

nature of sites around caves and repeated measures at sites. I fit negative binomial mixed models

using R package glmmADMB (Fournier et al. 2012). I used negative binomial mixed models

because bat activity data are counts, and variance was greater than the mean. Because I expected

nonlinear changes in bat activity over the sampling period, I compared fully parameterized

models with different polynomial structures on date for each species and species group. I used a

two-step information theoretic approach; first ranking models with polynomial structures on date

using Akaike’s Information Criterion corrected for small sample size (AICc from package

MuMIn; Bartoń 2015; Burnham and Anderson 2002), then using the best-supported polynomial

structure for all subsequent candidate models representing a priori hypotheses. If models were

competing (ΔAICc<2) I used the model with the lowest polynomial order, to avoid overfitting. I

centered and scaled all continuous predictors to allow me to assess main effects of interactions

(Schielzeth 2010).

9

I included a set of models for all Myotis bat species grouped together (hereafter Myotis

spp.), and all cave-dwelling bat species grouped together (Myotis spp. with PESU and EPFU,

hereafter ‘cavebats’). The ‘cavebats’ grouping was analyzed because all cave-dwelling species in

my study range are impacted by WNS, and I sought to identify drivers of general bat activity in

autumn and spring (Frick et al. 2016). Similarly, I analyzed the Myotis spp. because all Myotis

species are the genus most heavily impacted by WNS, and to assess activity with less possible

bias from species misclassification associated with automated identification software for the

genus. I also ran models for MYSO, MYSE, and EPFU individually because I expected

differences in activity patterns among species. For example, EPFU represent a cave-dwelling

species with rather different life history than most Myotis spp; notably EPFU are impacted less

by WNS and, in the southern United States, tend to prefer human structures for roost sites (Kunz

2013, Frank et al. 2014). I compared competing hypotheses using AICc (Burnham and Anderson

2002). Two cave-dwelling species, MYLE and PESU, were excluded from the individual species

analysis due to limited data. Little brown bats were excluded because hibernating population

numbers are and were historically very different between sampled hibernacula (R. Reynolds,

Virginia Department of Game and Inland Fisheries, pers. comm., Powers et al. 2015). To test for

the existence of interacting effects of date and distance to hibernacula on bat activity, I fit post

hoc models for each species/group for both autumn and spring.

Results

I acoustically sampled 22 sites around three caves, in autumn 2015, spring 2016, autumn

2016, and spring 2017. I sampled for 68 and 97 nights over autumn 2015 and 2016, and for 49

and 56 nights during spring 2016 and 2017, respectively. Though my objective was to sample

10

continuously across each season, due to detector failure, and inaccessibility due to weather, some

detector sites were not sampled continuously. I limited analyses to include only call data with a

minimum of three call pulses, to help optimize sensitivity and specificity.

Autumn activity patterns

My best supported model describing Myotis spp. activity received 88 percent of the

overall model support and contained the following variables: date, a 2nd order polynomial term

on date, year, mean daily temperature, change in mean daily temperature, mean daily wind

speed, change in mean daily wind speed, change in binary precipitation, distance to cave, and an

interaction between date and mean daily temperature (Table 1-3). No other models were

competing. Among continuous predictors, date and mean daily temperature had the largest effect

sizes (Table 1-3). Myotis spp. activity was substantially greater proximal to cave entrances

relative to distal sites (Table 1-3), and decreased over the season. However, both were related

positively to mean daily temperature. Temperature and date interacted, such that temperature had

a stronger impact on activity later in the season (Figure 1-3). Although contained in the best

supported model, change in mean daily temperature, mean daily wind speed, change in mean

daily wind speed, and change in binary precipitation had minimal effect sizes (Table 1-3).

Overall Myotis spp. activity was lower in 2016 than 2015 (Figure 1-3). Post-hoc modelling

indicated that there was no substantial interaction between date and distance to hibernacula

(Table 1-4).

The best supported model describing ‘cavebat’ activity contained the following variables:

date, a 2nd order polynomial term on date, year, mean daily temperature, change in mean daily

temperature, mean daily wind speed, change in mean daily wind speed, change in binary

precipitation, distance to cave, and an interaction between date and mean daily temperature

11

(Table 1-5). Among those, date, mean daily temperature, and distance to cave had the largest

effect sizes (Table 1-5). ‘Cavebat’ activity was substantially greater proximal to cave entrances

relative to distal sites, and decreased over the season, but both were related positively to mean

daily temperature. Temperature and date interacted, such that temperature had a stronger impact

on activity later in the season (Figure 1-4). Although contained in the best supported model,

change in mean daily temperature, mean daily wind speed, change in mean daily wind speed, and

change in binary precipitation had minimal effect sizes (Table 1-5). ‘Cavebat’ activity was

marginally lower in 2016 than 2015. Post-hoc modelling indicated that there was no substantial

interaction between date and distance to hibernacula (Table 1-6).

The best supported model describing EPFU activity contained the following variables:

date and its 4th order polynomial term, year, maximum daily temperature, change in maximum

daily temperature, maximum daily wind speed, change in maximum daily wind speed, binary

precipitation, and change in binary precipitation (Table 1-7 and Table 1-8). Only maximum daily

temperature had a large effect size (Figure 1-5, Table 1-8). Neither date nor year had a

substantial effect on activity level (Table 1-8). Although also contained in the best supported

model, change in maximum daily temperature, maximum daily wind speed, change in maximum

daily wind speed, binary precipitation, and change in binary precipitation had minimal effect

sizes (Table 1-8). Post-hoc modelling indicated that an interaction between date and distance to

hibernacula had a substantial effect on EPFU activity, such that activity levels decreased at distal

sites more rapidly than proximal to hibernacula (Figure 6 and Table 1-9).

The best supported model describing MYSE activity contained the following variables:

date, a 2nd order polynomial term on date, year, maximum daily temperature, change in

maximum daily temperature, binary precipitation, change in binary precipitation, and distance to

12

cave (Table 1-10 and Table 1-11). Among those, date and distance to cave had the largest effect

sizes. Activity of MYSE was substantially greater proximal to cave entrances relative to distal

sites (Table 1-11). MYSE activity decreased over the season and maximum daily temperature

had a marginal positive impact on MYSE activity (Figure 1-7). MYSE activity was marginally

lower in 2016 than 2015 (Table 1-11). Although also contained in the best supported model,

change in maximum daily temperature, binary precipitation, and change in binary precipitation

had minimal effect sizes (Table 1-11). Post-hoc modelling indicated that an interaction between

date and distance to hibernacula had a substantial effect on MYSE activity, such that activity

levels decreased at distal sites more rapidly than proximal to hibernacula (Table 1-12, Figure 1-

8).

The best supported model describing MYSO activity contained the following variables:

date, year, maximum daily temperature, change in maximum daily temperature, maximum daily

wind speed, change in maximum daily wind speed, binary precipitation, change in binary

precipitation, distance to cave, and an interaction between date and maximum daily temperature

(Table 1-13). Among those, date, year, distance to cave, mean maximum daily temperature, and

the interaction between date and maximum daily temperature had the largest effect sizes (Table

1-13). Activity of MYSO was substantially greater proximal to cave entrances relative to distal

sites and decreased over the season, but both were related positively to maximum daily

temperature. Temperature and date interacted, such that temperature had a stronger impact late in

the season (Figure 1-9). Although contained in the best supported model, change in maximum

daily temperature, maximum daily wind speed, change in maximum daily wind speed, binary

precipitation, and change in binary precipitation had minimal effect sizes (Table 1-13). Activity

of MYSE was lower in 2016 than 2015. Post-hoc modelling indicated that an interaction

13

between date and distance to hibernacula had a substantial effect on MYSO activity, such that

activity levels early in autumn were higher at sites two and three km away from caves compared

to sites one km away (Table 1-14). MYSO activity decreased more rapidly at these more distal

sites than sites one km away from the cave (Table 1-14, Figure 1-10).

Spring Activity Patterns

The best supported model describing Myotis spp. activity contained the following

variables: date and its 3rd order polynomial term, year, mean daily temperature, change in mean

daily temperature, mean daily wind speed, change in mean daily wind speed, change in binary


(Table 1-15). Among those, mean daily temperature, mean daily wind speed, and distance to

cave had the largest effect sizes (Table 1-15). Myotis spp. activity was substantially greater

proximal to cave entrances relative to distal sites (Table 1-15). Myotis spp. activity was

consistently low over the season, only increasing slightly later in the spring, but neither date nor

year had a substantial effect on activity level. Myotis spp. activity was related positively to mean

daily temperature, but negatively related to mean daily wind speed. Temperature and date

interacted, such that temperature had a stronger impact early in the season (Figure 1-11).

Although also contained in the best supported model, change in mean daily temperature, change

in mean daily wind speed, and change in binary precipitation had minimal effect sizes (Table 1-

15). Post-hoc modelling indicated that there was no substantial interaction between date and

distance to hibernacula (Table 1-16).

The best supported model describing ‘cavebat’ activity contained the following variables:

date and its 3rd order polynomial, year, maximum daily temperature, minimum daily temperature,

change in maximum daily temperature, change in minimum daily temperature, maximum daily

14



(Table 1-17). Among those, maximum daily temperature and distance to cave had the largest

effect sizes (Table 1-17). ‘Cavebat’ activity was positively related to maximum daily

temperature, and substantially higher proximal to cave entrances relative to distal sites (Figure 1-

12). Neither date nor year had a substantial effect on ‘cavebat’ activity level (Table 1-17).

Although also contained in the best supported model, change in minimum daily temperature,

change in minimum daily temperature, change in maximum daily temperature, maximum daily


precipitation, and the interaction between date and maximum daily temperature had minimal

effect sizes (Table 1-17). Post-hoc modelling indicated that an interaction between date and

distance to hibernacula had a substantial effect on ‘cavebat’ activity at sites two km away from

caves, such that activity was lowest at sites two km away from caves early in the season but

increased to be higher than sites one km away later in the season (Table 1-18, Figure 1-13).

The best supported model describing EPFU activity contained the following variables:

date and its 3rd order polynomial, year, maximum daily temperature, minimum daily temperature,




(Table 1-19). Among those, maximum daily temperature, binary precipitation, and distance to

cave had the largest effect sizes (Table 1-19). Big brown bat activity was positively related to

maximum daily temperature and negatively related to daily precipitation (Figure 1-14). Activity

was higher proximal to cave entrances relative to distal sites (Table 1-19). Neither date nor year

15

had a substantial effect on EPFU activity level. Although also contained in the best supported

model, minimum daily temperature, change in maximum daily temperature, change in minimum

daily temperature, maximum daily wind speed, change in maximum daily wind speed, change in

binary precipitation, and the interaction between date and maximum daily temperature had

minimal effect sizes (Table 1-19). Post-hoc modelling indicated that an interaction between date

and distance to hibernacula had a substantial effect on EPFU activity, such that activity levels

increased at sites one km and two km away from hibernacula throughout the spring while

activity proximal to hibernacula displayed a unimodal peak early in the season (Table 1-20,

Figure 1-15).

The best supported model describing MYSE activity contained the following variables:

date, year, maximum daily temperature, minimum daily temperature, change in maximum daily

temperature, change in minimum daily temperature, maximum daily wind speed, change in

maximum daily wind speed, binary precipitation, change in binary precipitation, and distance to

cave (Table 1-21). Among those, minimum daily temperature and distance to cave had the

largest effect sizes (Table 1-21). MYSE activity was related positively to minimum daily

temperature whereas activity was substantially greater proximal to cave entrances relative to

distal sites (Figure 1-16). Neither date nor year had a substantial effect on MYSE activity level

(Table 1-21). Although also contained in the best supported model, maximum daily temperature,


wind speed, change in maximum daily wind speed, binary precipitation, and change in binary

precipitation had minimal effect sizes (Table 1-21). Post-hoc modelling indicated that an

interaction between date and distance to hibernacula had a substantial effect on MYSE activity,

16

such that activity levels increased at most distal sites (two km and three km away) more rapidly

than sites closer (one km away) and sites proximal to hibernacula (Table 1-22, Figure 1-17).

The best supported model describing MYSO activity contained the following variables:

date and its 3rd order polynomial term, year, mean daily temperature, change in mean daily

temperature, mean daily wind speed, change in mean daily wind speed, change in binary


(Table 1-23). Among those, mean daily temperature, mean daily wind speed, distance to cave,

and the interaction between date and mean daily temperature had the largest effect sizes (Table

1-23). Mean daily temperature and date interacted such that temperature had a greater impact

earlier in the season (Figure 1-18). Indiana bat activity was positively related to mean daily

temperature, but negatively related to mean daily wind speed (Figure 1-19). Indiana bat activity

was substantially greater proximal to cave entrances relative to distal sites (Table 1-23). Neither

date alone nor year had a substantial effect on MYSO activity level. Although also contained in

the best supported model, change in mean daily temperature, change in mean wind speed, and

change in binary precipitation had minimal effect sizes (Table 1-23). Post-hoc modelling

indicated that there was no substantial interaction between date and distance to hibernacula

(Table 1-24).

Discussion

Autumn activity varied among species and among species groups, but my results largely

were consistent with a priori expectations. In general, bat activity was most related to ambient

temperatures during autumn. In spring, bat activity was most related to ambient temperatures, but

related less closely to date. Based on differing life histories, I expected species-specific

responses to ambient conditions and distance to caves in temperate environments (Aldridge and

17

Rautenbach 1987, Bergeson et al. 2013, Jachowski et al. 2014, Norquay and Willis 2014). The

results corroborate previous research indicating ambient temperatures are positively related to

general bat activity across seasons (Parsons et al. 2003, Kunz 2013, Bender and Hartman 2015,

Meyer et al. 2016, Bernard and McCracken 2017), and specifically show this relationship exists

around hibernacula.

Although temperature and date generally had large effects on overall bat activity, not all

species/groups followed this exact pattern. I expected a priori that activity for all individual

cave-dwelling species would contract and concentrate around cave entrances through autumn,

but my results indicated this pattern existed only for MYSO, MYSE and EPFU. The substantial

interacting effects of temperature and date on Myotis spp., ‘cavebats’, and MYSO autumn

activity likely exist due to metabolic/thermal costs and/or benefits relating to prey availability

(Bender and Hartman 2015, Bernard and McCracken 2017). Prey resources of insectivorous bats

become scarce at lower ambient temperatures (Williams 1940, Meyer et al. 2016). Autumn

activity of EPFU largely depended on maximum daily temperature, a finding similar to previous

research indicating EPFU activity through the winter months is influenced by temperature (Klüg-

Baerwald et al. 2016). Research has found that unlike Myotis spp., EPFU appear to be less

impacted by WNS (Francl et al. 2012), and may have fewer physiological constraints due to

larger body sizes, allowing for greater proportional late fall and winter energy expenditures

during the hibernation season therefore there was no evidence of interacting effects of

temperature and date on EPFU. It is possible EPFU can also return to typical summer-type roosts

(human structures/barns) during fall and winter activity, which may afford thermal and metabolic

benefits (Whitaker Jr. and Gummer 1992). The best supported model did not include the at-cave

variable, indicating that EPFU activity was generally more widespread across the landscape

18

during autumn. This is in contrast with other cave-dwelling species and species groups in my

study. This is likely a result of species-specific foraging strategies, but also could be attributed to

species-specific roost selection and preference; EPFU may fly over 1.6 km to foraging areas

from roost sites, further, on average, than the Myotis spp. that occurred at my study sites

(Brigham 1991, Menzel et al. 2002, 2005, Lowe 2012). The verified (Brack et al. 2005, Powers

et al. 2015) and speculative (Weishampel et al. 2011) existence of other hibernacula in close

proximity (within 3 km) to my study sites might have had an influence on each of the species and

species groups through the autumn swarm period.

The majority of activity for all species/groups occurred in early autumn and declined by

mid-October, yet some activity (across species/groups) continued to occur through the fall

swarm season at warmer conditions, suggesting that ambient conditions may partially regulate

swarm activity and cave entry dynamics. Furthermore, by influencing bat activity and prey

availability, ambient temperature may ultimately influence the body condition of bats entering

hibernation (Hall 1962, Jonasson and Willis 2011). In any given year, above-average ambient

temperatures may delay bats’ entry into hibernation by allowing bats to remain active later in

autumn. This could presumably lead to shorter hibernation periods; potentially reducing fungal

loads and therefore WNS-related mortality (Reeder et al. 2012, Langwig et al. 2015). Warmer

temperatures are linked to increased prey availability (Meyer et al. 2016a) and may lead to

superior body conditions as bats enter hibernation, which also may affect overwinter survival as

well as reproductive output in the following seasons (Frick et al. 2012). Conversely, it may be

that colder ambient temperatures during the pre-hibernation period lead to improved fat reserves,

as seen in a European bat species, the brown long-eared bat (Plecotus auritus; Speakman and

Rowland 1999). Prior to hibernation, warmer temperatures lead to greater activity on the

19

landscape, but this could cause bats to be susceptible to other threats such as wind energy

development or forest management, if day-roost loss occurred later into autumn than previously

anticipated. Furthermore, the exact biological triggers for immergence into hibernation are not

fully understood for bats, and these triggers may be more closely linked to fat reserves than to

ambient conditions and/or prey availability (Jonasson and Willis 2011). Although cave-dwelling

bats, and specifically MYSO, usually hibernate where they swarm (LaVal et al. 1977, Schaik et

al. 2015), pre-hibernation long-distance movements between hibernacula/swarming sites are not

uncommon (Cope and Humphrey 1977, Parsons et al. 2003). Ambient temperatures could

certainly affect the movement patterns of bats between hibernacula/swarming sites, and thus may

affect mating dynamics and immergence phenology. Further understanding the effects of

temperature on swarming behavior may allow managers to determine the most crucial swarming

periods, and thus plan protective measures around hibernacula more efficiently.

Similar to autumn, spring activity also was species-specific, but in general, activity was

less related to date. Daily temperatures were the driving climatic variable that impacted activity

for all species and groups in the spring, supporting a priori expectations. Findings also agreed

with previous research that showed bat emergence from hibernacula in the spring was related

positively to ambient temperatures and, to a lesser extent, photoperiod (Meyer et al. 2016).

Czenze and Willis (2015) found MYLU spring emergence was correlated most with a drop in

barometric pressure, rather than temperatures outside a hibernacula. Furthermore, Meyer et al.

(2016) found that timing of emergence was not associated with temperatures inside hibernacula.

However, I found that ambient temperatures substantially affect bat activity post-emergence,

across species/groups. Temperatures at roost sites within hibernacula change very little

regardless of changing outside ambient temperatures (Czenze and Willis 2015, Meyer et al.

20

2016), but temperatures and temperature fluctuations at roost sites outside hibernacula are

readily perceived by bats (Hamilton and Barclay 1994, Callahan et al. 1997). Ambient

temperatures post-emergence likely are the principal indicators for bats’ activity in the spring.

Although the effect size of date in spring was smaller than in autumn, there was an

interaction between date and distance to hibernacula for ‘cavebats’, MYSE, and EPFU. Activity

of ‘cavebats’ at distal sites increased at different rates towards the end of April, possibly

reflecting the progression of movement of bats outwards from hibernacula towards summer

ranges. Similarly, by mid-April, MYSE activity increased at a greater rate at most distal sites (2

and 3 km away) whereas activity decreased at sites one km from caves, likely displaying an

outward movement of bats post-emergence. Spring activity of EPFU followed a comparable

trend, with activity increasing faster at distal sites than at sites proximal to caves. Although the

interaction between date and distance to hibernacula had large effect sizes, confidence intervals

for estimates of ‘cavebats’, MYSE, and EPFU activity were large and overlapping, suggesting

small trends.

Northern long-eared bat activity was most related to minimum daily temperature whereas

activity of all other species/groups activity was more closely related to mean daily or maximum

daily temperature likely due to species-specific foraging strategies and differences in prey base

(Aldridge and Rautenbach 1987, Lacki et al. 2007). Lepidopterans (moths and butterflies) are

important prey of MYSE (Caceres and Barclay 2000, Carter et al. 2003, Dodd et al. 2012) and

are less active at cooler temperatures and generally less abundant during the dormant, non-

growing season (Williams 1940, Meyer et al. 2016). However, data on MYSE prey selection are

limited and chiefly from summer months, with knowledge of prey resources in the spring lacking

(Carter et al. 2003, Dodd et al. 2012). It is possible that MYSE may perceive minimum daily

21

temperatures as an indicator of prey availability whereas other species more readily perceive

maximum or mean daily temperatures as indicators of prey availability (Wojciechowski et al.

2007). Further research is warranted on spring prey availability in relation to temperature to

determine how and why temperature drives species-specific activity patterns during the spring

emergence period (Ciechanowski et al. 2007).

Although the USFWS seasonal tree clearing window allows clearing activities through

March 31st, I observed a substantial amount of activity of MYSO and all cave-dwelling species

well before the end of March. Furthermore, many hibernacula are used by both MYSO and

MYSE during the winter months, and summer ranges overlap significantly (Trani et al. 2007,

USFWS 2015, 2017b). I observed extremely low MYSE activity relative to other species,

including MYSO, which supports previous research indicating MYSE are among the species

most impacted by WNS and are functionally absent in many parts of the region (Francl et al.

2012, Powers et al. 2015, Reynolds et al. 2016). These results suggest that tree-clearing activities

in the spring could affect MYSO and MYSE in the central Appalachians, especially when early

spring temperatures are unseasonably warm. Tree-clearing activities during the spring near

hibernacula prior to March 31 may have the potential, albeit it small, to exacerbate WNS-related

population declines in the central Appalachians. Alternately, my data support the adequacy of the

spatial and temporal extent of these buffers during the autumn swarm season, as activity had

declined to negligible levels prior to the November 15th clearing date. Currently, the Indiana Bat

Protection and Enhancement Plan Guidelines (USFWS 2009) provides for flexibility and

adjustments to tree clearing restriction dates based on localized fall swarming and spring

emergence data within the range of MYSO. However, data regarding bat activity around

hibernacula during these seasons are scant, offering few incentives to change the clearing

22

restriction dates. Due to the physiological vulnerability of bats following hibernation when their

fat reserves are depleted most and prey resources are less abundant or highly variable in

availability, managers should prioritize maintaining habitats surrounding hibernacula to ensure

adequate foraging and roosting opportunities.

Current land use and/or active land management around hibernacula may impact both

MYSE and MYSO during the autumn and spring. Prior to WNS, many of these impacts could

have been considered negligible, but additive mortality factors could further imperil already

diminutive populations following the impacts of WNS. My data suggest that concluding tree-

clearing activities by early March could more adequately protect physiologically-stressed MYSO

as they resume behaviors on the landscape. Streamlining management strategies will increase the

chance for populations’ persistence and recovery by avoiding ‘take’ in seasons critical for

successful reproduction. Extending these protections to a number of hibernacula also would

undoubtedly benefit other imperiled bat species’ populations not considered here. Finally, a

better regional comprehension of the effects of ambient conditions on autumn and spring bat

activity will help land managers and researchers plan effective surveys to further understand

post-WNS ecology of bats.

23

Literature Cited

Aldridge, H. D. J. N., and I. L. Rautenbach. 1987. Morphology, echolocation and resource

partitioning in insectivorous bats. Journal of Animal Ecology 56:763–778.

Barclay, R. M. R., M. B. Fenton, and D. W. Thomas. 1979. Social behavior of the little brown

bat, Myotis lucifugus: II. Vocal Communication. Behavioral Ecology and Sociobiology

6:137–146.

Bartoń, K. A. 2015. Package “MuMIn” | Multi-Model Inference.

https://www.rdocumentation.org/packages/MuMIn/versions/1.15.6/topics/MuMIn-

package. Accessed 12 Sep 2017.

Bender, M. J., and G. D. Hartman. 2015. Bat activity increases with barometric pressure and

temperature during autumn in central Georgia. Southeastern Naturalist 14:231–242.

Bergeson, S. M., T. C. Carter, and M. D. Whitby. 2013. Partitioning of foraging resources

between sympatric Indiana and little brown bats. Journal of Mammalogy 94:1311–1320.

Bernard, R. F., and G. F. McCracken. 2017. Winter behavior of bats and the progression of

white-nose syndrome in the southeastern United States. Ecology and Evolution 7:1487–

1496.

Blehert, D. S., A. C. Hicks, M. Behr, C. U. Meteyer, B. M. Berlowski-Zier, E. L. Buckles, J. T.

H. Coleman, S. R. Darling, A. Gargas, R. Niver, J. C. Okoniewski, R. J. Rudd, and W. B.

Stone. 2009. Bat white-nose syndrome: an emerging fungal pathogen? Science 323:227–

227.

Brack, V. 2006. Autumn activity of Myotis sodalis (Indiana Bat) in Bland County, Virginia.

Northeastern Naturalist 13:421–434.

Brack, V., R. Reynolds, W. Orndorff, J. Zokaites, and C. Zokaites. 2005. Bats of Skydusky

Hollow, Bland County, Virginia. Virginia Journal of Science 56 (2): 93-106.

Braun, E. L. 1950. Deciduous forests of eastern North America. Blakiston Company,

Philadelphia, Pennsylvania.

Brigham, R. M. 1991. Flexibility in foraging and roosting behaviour by the big brown bat

(Eptesicus fuscus). Canadian Journal of Zoology 69:117–121.

Britzke, E. R., A. C. Hicks, S. L. Von Oettingen, and S. R. Darling. 2006. Description of spring

roost trees used by female Indiana bats (Myotis sodalis) in the Lake Champlain Valley of

Vermont and New York. The American Midland Naturalist 155:181–187.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a

practical information-theoretic approach. Springer, New York.

24

Burns, L. E., and H. G. Broders. 2015. Maximizing mating opportunities: higher autumn

swarming activity in male versus female Myotis bats. Journal of Mammalogy 96:1326–

1336.

Caceres, M. C., and R. M. R. Barclay. 2000. Myotis septentrionalis. Mammalian Species 1–4.

Caire, W., R. K. LaVal, M. L. LaVal, and R. Clawson. 1979. Notes on the ecology of Myotis

keenii (Chiroptera, Vespertilionidae) in Eastern Missouri. The American Midland

Naturalist 102:404–407.

Callahan, E. V., R. D. Drobney, and R. L. Clawson. 1997. Selection of summer roosting sites by

Indiana bats (Myotis sodalis) in Missouri. Journal of Mammalogy 78:818–825.

Carter, T. C., M. A. Menzel, S. F. Owen, J. W. Edwards, J. M. Menzel, and W. M. Ford. 2003.

Food habits of seven species of bats in the Allegheny Plateau and Ridge and Valley of

West Virginia. Northeastern Naturalist 10:83–88.

Ciechanowski, M., T. Zając, A. Biłas, and R. Dunajski. 2007. Spatiotemporal variation in

activity of bat species differing in hunting tactics: effects of weather, moonlight, food

abundance, and structural clutter. Canadian Journal of Zoology 85:1249–1263.

Cope, J. B., and S. R. Humphrey. 1977. Spring and autumn swarming behavior in the Indiana

Bat, Myotis sodalis. Journal of Mammalogy 58:93–95.

Czenze, Z. J., and C. K. R. Willis. 2015. Warming up and shipping out: arousal and emergence

timing in hibernating little brown bats (Myotis lucifugus). Journal of Comparative

Physiology. B, Biochemical, Systemic, and Environmental Physiology 185:575–586.

Davis, W. H., and H. B. Hitchcock. 1965. Biology and migration of the Bat, Myotis lucifugus, in

New England. Journal of Mammalogy 46:296–313.

Dodd, L. E., E. G. Chapman, J. D. Harwood, M. J. Lacki, and L. K. Rieske. 2012. Identification

of prey of Myotis septentrionalis using DNA-based techniques. Journal of Mammalogy

93:1119–1128.

[ESA] Endangered Species Act of 1973, Pub. L. No. 93-205, 87 Stat. 884 (Dec. 28, 1973).

Available: https://www.fws.gov/endangered/esa-library/pdf/ESAall.pdf.

Ewing, W. G., E. H. Studier, and M. J. O’Farrell. 1970. Autumn fat deposition and gross body

composition in three species of Myotis. Comparative Biochemistry and Physiology

36:119–129.

Ford, M. 2017. Kaleidoscope 4.2.0. Report prepared for USFWS. Available:

https://www.fws.gov/midwest/endangered/mammals/inba/surveys/pdf/USGSTestReport1

0KPro4_2_0multi-setting.pdf.

25

Ford, W. M., M. A. Menzel, J. L. Rodrigue, J. M. Menzel, and J. B. Johnson. 2005. Relating bat

species presence to simple habitat measures in a central Appalachian forest. Biological

Conservation 126:528–539.

Fournier, D. A., H. J. Skaug, J. Ancheta, J. Ianelli, A. Magnusson, M. N. Maunder, A. Nielsen,

and J. Sibert. 2012. AD Model Builder: Using automatic differentiation for statistical

inference of highly parameterized complex nonlinear models.

Francl, K. E., W. M. Ford, D. W. Sparks, and V. Brack. 2012. Capture and reproductive trends in

summer bat communities in West Virginia: Assessing the Impact of White-Nose

Syndrome. Journal of Fish and Wildlife Management 3:33–42.

Frank, C. L., A. Michalski, A. A. McDonough, M. Rahimian, R. J. Rudd, and C. Herzog. 2014.

The resistance of a North American bat species (Eptesicus fuscus) to White-Nose

Syndrome (WNS). PLoS ONE 9:e113958.

Frick, W. F., S. J. Puechmaille, and C. K. R. Willis. 2016. White-Nose Syndrome in bats. Pages

245–262 in. Bats in the Anthropocene: Conservation of Bats in a Changing World.

Springer, Cham.

Frick, W. F., P. M. Stepanian, J. F. Kelly, K. W. Howard, C. M. Kuster, T. H. Kunz, and P. B.

Chilson. 2012. Climate and weather impact timing of emergence of bats. B. Fenton,

editor. PLoS ONE 7:e42737.

Hall, J. S. 1962. A life history and taxonomic study of the Indiana bat, Myotis sodalis. Reading

Public Museum and Art Gallery.

Hamilton, I. M., and R. M. R. Barclay. 1994. Patterns of daily torpor and day-roost selection by

male and female big brown bats (Eptesicus fuscus). Canadian Journal of Zoology

72:744–749.

Jachowski, D. S., C. A. Dobony, L. S. Coleman, W. M. Ford, E. R. Britzke, and J. L. Rodrigue.

2014. Disease and community structure: white-nose syndrome alters spatial and temporal

niche partitioning in sympatric bat species. Diversity Distrib., 20: 1002–1015.

doi:10.1111/ddi.12192

Jonasson, K. A., and C. K. R. Willis. 2011. Changes in body condition of hibernating bats

support the thrifty female hypothesis and predict consequences for populations with

White-Nose Syndrome. PLoS ONE 6(6): e21061.

Klüg-Baerwald, B. J., L. E. Gower, C. Lausen, and R. M. Brigham. 2016. Environmental

correlates and energetics of winter flight by bats in Southern Alberta, Canada. Canadian

Journal of Zoology, 2016, 94:829-836.

Kunz, T. H. 2013. Ecology of Bats. Springer Science & Business Media. Boston, MA.

Kunz, T. H., J. A. Wrazen, and C. D. Burnett. 1998. Changes in body mass and fat reserves in

pre-hibernating little brown bats (Myotis lucifugus). Écoscience, 5:1, 8-17.

26

Kurta, A., and S. W. Murray. 2002. Philopatry and migration of banded Indiana bats (Myotis

sodalis) and effects of radio transmitters. Journal of Mammalogy 83:585–589.

M B Fenton. 2007. Bats in forests: Conservation and management. Edited by Michael J Lacki,

John P Hayes, and Allen Kurta; Foreword by Merlin D Tuttle. The Quarterly Review of

Biology 82, 4:427-428.

Langwig, K. E., W. F. Frick, R. Reynolds, K. L. Parise, K. P. Drees, J. R. Hoyt, T. L. Cheng, T.

H. Kunz, J. T. Foster, and A. M. Kilpatrick. 2015. Host and pathogen ecology drive the

seasonal dynamics of a fungal disease, white-nose syndrome. Proceedings of the Royal

Society of London B: Biological Sciences 282:20142335.

LaVal, R. K., R. L. Clawson, M. L. LaVal, and W. Caire. 1977. Foraging behavior and nocturnal

activity patterns of Missouri bats, with emphasis on the endangered species Myotis

grisescens and Myotis sodalis. Journal of Mammalogy 58:592–599.

Lowe, A. J. 2012. Swarming behaviour and fall roost-use of little brown (Myotis lucifugus), and

northern long-eared bats (Myotis septentrionalis) in Nova Scotia, Canada. (Master’s

thesis). Retrieved from http://library2.smu.ca/xmlui/handle/01/25125.

Menzel, J. M., M. A. Menzel, J. C. Kilgo, W. M. Ford, J. W. Edwards, and G. F. McCracken.

2005. Effect of habitat and foraging height on bat activity in the coastal plain of South

Carolina. The Journal of Wildlife Management 69:235–245.

Menzel, M. A., J. M. Menzel, S. B. Castleberry, J. Ozier, W. M. Ford, and J. W. Edwards. 2002.

Illustrated key to skins and skulls of bats in the southeastern and mid-Atlantic states.

USDA Forest Service, Research Note NE-376, Newton Square, PA.

Meyer, G. A., J. A. Senulis, and J. A. Reinartz. 2016a. Effects of temperature and availability of

insect prey on bat emergence from hibernation in spring. Journal of Mammalogy

97:1623–1633.

Meyer, G. A., J. A. Senulis, and J. A. Reinartz. 2016b. Effects of temperature and availability of

insect prey on bat emergence from hibernation in spring. Journal of Mammalogy

97:1623–1633.

Norquay, K. J. O., and C. K. R. Willis. 2014. Hibernation phenology of Myotis lucifugus. Journal

of Zoology 294:85–92.

Ozgul, A., D. Z. Childs, M. K. Oli, K. B. Armitage, D. T. Blumstein, L. E. Olson, S. Tuljapurkar,

and T. Coulson. 2010. Coupled dynamics of body mass and population growth in

response to environmental change. Nature 466:482–485.

Parsons, K. N., G. Jones, and F. Greenaway. 2003. Swarming activity of temperate zone

microchiropteran bats: effects of season, time of night and weather conditions. Journal of

Zoology 261:257–264.

27

Pettit, J. L., and J. M. O’Keefe. 2017. Day of year, temperature, wind, and precipitation predict

timing of bat migration. Journal of Mammalogy 98:1236–1248.

Pinheiro, J., D. Bates, S. DebRoy, D. Sarkar, and S. Heisterkamp. 2017. nlme: Linear and

Nonlinear Mixed Effects Models. https://cran.r-

project.org/web/packages/nlme/index.html. Accessed 26 Sep 2017.

Karen, E. P., Richard, J. R., Orndorff, W., Ford, W. M., & Hobson, C. S. 2015. Post-White-nose

syndrome trends in Virginias cave bats, 2008-2013. Journal of Ecology and The Natural

Environment 7:113–123.

R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. http://www.R-project.org/.

Reeder, D. M., C. L. Frank, G. G. Turner, C. U. Meteyer, A. Kurta, E. R. Britzke, M. E. Vodzak,

S. R. Darling, C. W. Stihler, A. C. Hicks, R. Jacob, L. E. Grieneisen, S. A. Brownlee, L.

K. Muller, and D. S. Blehert. 2012. Frequent arousal from hibernation linked to severity

of infection and mortality in bats with White-Nose Syndrome. PLoS ONE 7:e38920.

Reynolds, R. J., K. E. Powers, W. Orndorff, W. M. Ford, and C. S. Hobson. 2016. Changes in

rates of capture and demographics of Myotis septentrionalis (Northern Long-eared Bat)

in Western Virginia before and after onset of White-nose Syndrome. Northeastern


Rivers, N. M., R. K. Butlin, and J. D. Altringham. 2005. Genetic population structure of

Natterer’s bats explained by mating at swarming sites and philopatry. Molecular Ecology

14:4299–4312.

van Schaik, J., R. Janssen, T. Bosch, A.-J. Haarsma, J. J. A. Dekker, and B. Kranstauber. 2015.

Bats swarm where they hibernate: compositional similarity between autumn swarming

and winter hibernation assemblages at five underground sites. PLoS ONE 10:e0130850.

Schielzeth, H. 2010. Simple means to improve the interpretability of regression coefficients.

Methods in Ecology and Evolution 1:103–113.

Schowalter, D. B. 1980. Swarming, reproduction, and early hibernation of Myotis lucifugus and

M. volans in Alberta, Canada. Journal of Mammalogy 61:350–354.

Speakman, J. R., and A. Rowland. 1999. Preparing for inactivity: How insectivorous bats deposit

a fat store for hibernation. Proceedings of the Nutrition Society 58:123–131.

Trani, M. K., W. M. Ford, and R. Brian. 2007. The land manager’s guide to mammals of the

South. Durham, NC: The Nature Conservancy; Atlanta, GA: U.S. Forest Service. 546 p.

USFWS. 2007. Indiana Bat (Myotis sodalis) Draft Recovery Plan: First Revision. U.S. Fish and

Wildlife Service, Fort Snelling, MN.

https://pdfs.semanticscholar.org/c2dc/e48a43504e3a44052a2df39fb1b720cb028c.pdf

Accessed 31 Aug 2017.

28

USFWS. 2009. Range-wide Indiana Bat Protection and Enhancement Plan Guidelines.

https://www.fws.gov/frankfort/pdf/INBATPEPGuidelines.pdf.

USFWS. 2017a. Indiana Bat Summer Survey Guidance - Automated Acoustic Bat ID Software

Programs.

https://www.fws.gov/midwest/endangered/mammals/inba/surveys/inbaAcousticSoftware.

html. Accessed 26 Oct 2017.

USFWS. 2017b. USFWS: Northern long-eared bat range maps.

https://www.fws.gov/midwest/endangered/mammals/nleb/nlebRangeMap.html. Accessed

1 Sep 2017.

USFWS. 2015. Indiana Bat Range Map and Recovery Units.

https://www.fws.gov/midwest/endangered/mammals/inba/RangeMapINBA.html.

Accessed 1 Sep 2017.

Wei, T., and V. Simko. 2016. corrplot: Visualization of a correlation matrix. https://cran.r-

project.org/web/packages/corrplot/index.html. Accessed 24 Aug 2017.

Weishampel, J., J. Hightower, A. Chase, D. Chase, and R. Patrick. 2011. Detection and

morphologic analysis of potential below-canopy cave openings in the karst landscape

around the Maya polity of Caracol using airborne Lidar. Journal of Cave and Karst

Studies 73:187–196.

Whitaker, J. O., and W. J. Hamilton. 1998. Mammals of the eastern United States. Cornell

University Press. Ithaca, New York.

Whitaker, J. O., and L. J. Rissler. 1992. Winter Activity of Bats at a Mine Entrance in Vermillion

County, Indiana. American Midland Naturalist 127:52–59.

Whitaker, J. O. Jr., and S. L. Gummer. 1992. Hibernation of the big brown bat, Eptesicus fuscus,

in buildings. Journal of Mammalogy 73:312–316.

Wildlife Acoustics - Overview of Kaleidoscope Pro 3 Analysis Software.

https://www.wildlifeacoustics.com/products/kaleidoscope-software-ultrasonic. Accessed

26 Oct 2017.

Williams, C. B. 1940. An analysis of four years captures of insects in a light trap. Part II. The

effect of weather conditions on insect activity; and the estimation and forecasting of

changes in the insect population. Transactions of the Royal Entomological Society of

London 90:227–306.

Wojciechowski, M. S., M. Jefimow, and E. Tęgowska. 2007. Environmental conditions, rather

than season, determine torpor use and temperature selection in large mouse-eared bats

(Myotis myotis). Comparative Biochemistry and Physiology Part A: Molecular &

Integrative Physiology 147:828–840.

29

Tables

Table 1-1: Variables used in candidate models representing hypotheses regarding bat activity

around three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015

and 2016, and spring 2016 and 2017. Variables were used in different combinations, and highly

correlated variables were not included within a single candidate model.

Variable Explanation

Date date

Year sampling year

Avg. Temp mean daily temperature

Max. Temp maximum daily temperature

Min. Temp minimum daily temperature

Δ Avg. Temp change in mean daily temperature from previous day

Δ Max. Temp change in maximum daily temperature from previous day

Δ Min. Temp change in minimum daily temperature from previous day

Max Wind maximum daily wind speed

Avg. Wind mean daily wind speed

Δ Max. Wind change in maximum daily wind speed from previous day

Δ Mean. Wind change in mean daily wind speed from previous day

Binary Precipitation binary precipitation

Δ Binary Precipitation change in binary precipitation from previous day

Cave Proximity at cave or not at cave

30




autumn 2015 and 2016, and spring 2016 and 2017.

Parameter Justification Supporting Literature

Date

Bat activity varies

in intensity and spatially by

date

(Parsons et al. 2003, Brack 2006,

Brooks 2009, Johnson et al. 2011)

Year

Bat abundance and

activity varies from

year to year, especially

in the wake of WNS

(Blehert et al. 2009, Frick et al.

2010, Francl et al. 2012)

Mean daily temperature Daily temperatures affect

bat activity

(Parsons et al. 2003, Bender and

Hartman 2015, Meyer et al. 2016)

Maximum daily

temperature

Daily temperatures affect

bat activity



Minimum daily

temperature

Daily temperatures affect

bat activity



Change in mean daily

temperature from

previous day

Bats can likely sense even

small temperature changes

and could adjust behavior

accordingly

(Kunz 2013, Meyer et al. 2016)

Change in maximum

daily temperature from

previous day




accordingly


Change in minimum daily

temperature from

previous day




accordingly


Maximum daily wind

speed

Bat activity generally

decreases with high wind

speeds

(Baerwald and Barclay 2011,

Weller and Baldwin 2012, Smith

and McWilliams 2016)

31

Table 1-2 (Continued)


Mean daily wind speed



speeds




Change in maximum

daily wind speed from

previous day

Bats likely perceive changes

in wind speed and adjust

behavior accordingly




Change in mean daily

wind speed from previous

day

Bats likely perceive changes

in wind speed and adjust

behavior accordingly




Daily binary precipitation

Precipitation reduces bat

activity (Parsons et al. 2003, Kunz 2013)

Change in daily binary

precipitation from

previous day

Bats likely perceive a

changes in precipitation and

adjust behavior accordingly

(Parsons et al. 2003, Kunz 2013)

At cave/not at cave Activity is concentrated at

hibernacula entrances

(Burns and Broders 2015, Schaik et

al. 2015)

32





2016. An asterisk (*) between predictors indicates an interaction.

Variable β Lower CI Upper CI

(Intercept) 3.848 2.046 5.651

Date -0.810 -0.958 -0.661

Date2 -0.057 -0.186 0.072

Year 2016 -0.516 -0.717 -0.316

Avg. Temp 0.493 0.336 0.650

Δ Avg. Temp -0.204 -0.300 -0.107

Avg. Wind -0.143 -0.305 0.020

Δ Mean. Wind -0.143 -0.265 -0.022

Δ Binary Precipitation -0.302 -0.489 -0.116

Distal Sites -3.095 -4.609 -1.582

Date*Avg. Temp 0.466 0.315 0.616

33







(Intercept) 3.881 2.068 5.694

Date -0.661 -0.927 -0.394

Date2 -0.061 -0.190 0.068

Year 2016 -0.519 -0.720 -0.318

Avg. Temp 0.499 0.341 0.657

Δ Avg. Temp -0.205 -0.302 -0.107

Avg. Wind -0.144 -0.306 0.019

Δ Mean. Wind -0.141 -0.263 -0.020


Distal Sites -3.129 -4.647 -1.610

Date*Avg. Temp 0.477 0.325 0.630

Date*Cave Proximity -0.181 -0.451 0.088

34






(*) between predictors indicates an interaction.


(Intercept) 3.855 2.271 5.439

Date -0.664 -0.793 -0.535

Date2 0.036 -0.077 0.150

Year 2016 -0.432 -0.613 -0.250

Avg. Temp 0.638 0.499 0.776

Δ Avg. Temp -0.145 -0.231 -0.059

Avg. Wind -0.028 -0.166 0.111

Δ Mean. Wind -0.131 -0.238 -0.024


Distal Sites -2.667 -3.955 -1.378

Date*Avg. Temp 0.413 0.284 0.543

35








(Intercept) 3.824 2.24 5.408

Date -0.493 -0.742 -0.244

Date2 0.109 -0.125 0.344

Year 2016 -0.433 -0.614 -0.252

Avg. Temp 0.643 0.504 0.782

Δ Avg. Temp -0.152 -0.238 -0.067

Avg. Wind -0.043 -0.181 0.096

Δ Mean. Wind -0.129 -0.235 -0.022


Distance to Cave1km -2.492 -3.976 -1.009



Date*Avg. Temp 0.432 0.3 0.565

Date*Distance to Cave1km -0.119 -0.416 0.179


Date*Distance to Cave3km -0.378 -0.663 -0.093

Date2*Distance to Cave1km -0.188 -0.458 0.081

Date2*Distance to Cave2km 0.119 -0.143 0.381


36

Table 1-7: Rankings of models predicting Eptesicus fuscus (big brown bat) activity around three

caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016,

with k (number of parameters), Akaike’s information criteria (AIC) value, Akaike’s information

criteria (AICc) value corrected for small sample size, difference in AICc value between best

supported model and ith model (ΔAICc), wi (model weight), and ERi (evidence ratio).

Variable k AIC AICc ΔAICc wi ERi

Date + Date2 + Date3 + Date4 +

year + Max. Temp + Δ Max.

Temp + Max Wind + Δ Max.

Wind +

Binary Precipitation + Δ Binary

Precipitation

12.0 3699.4 3699.6 0.0 0.5 1.0


year +Date*Max. Temp + Min.

Temp + Δ Max. Temp + Δ Min.

Temp +

Max Wind + Δ Max. Wind +


Precipitation + Cave Proximity

16.0 3700.5 3700.9 1.3 0.2 1.9


year + Max. Temp + Δ Max.

Temp +

Max Wind + Δ Max. Wind +



13.0 3701.0 3701.2 1.6 0.2 2.3

37



Appalachians, Virginia and West Virginia, during autumn 2015 and 2016.


(Intercept) -1.298 -2.049 -0.547

Date 0.071 -0.222 0.364

Date2 0.139 -0.257 0.534

Date3 -0.114 -0.243 0.016

Date4 -0.056 -0.182 0.070

Year 2016 -0.038 -0.340 0.264

Max. Temp 1.222 0.983 1.462

Δ Max. Temp 0.165 0.012 0.318

Max Wind 0.296 0.109 0.483

Δ Max. Wind -0.068 -0.240 0.104

Binary Precipitation1 -0.201 -0.509 0.108

Δ Binary Precipitation -0.230 -0.549 0.089

38



Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. An asterisk (*)

between predictors indicates an interaction.


(Intercept) -0.704 -2.541 1.132

Date 0.941 0.509 1.373

Date2 0.006 -0.378 0.390

Date3 -0.131 -0.262 0.000

Date4 -0.028 -0.151 0.094

Year 2016 0.010 -0.283 0.303

Max. Temp 1.213 0.977 1.449

Δ Max. Temp 0.140 -0.009 0.290

Max Wind 0.202 0.018 0.386

Δ Max. Wind -0.094 -0.263 0.075



Distance to Cave1km -1.380 -3.609 0.849






39

Table 1-10: Rankings of models predicting Myotis septentrionalis (northern long-eared bat)


autumn 2015 and 2016, with k (number of parameters), Akaike’s information criteria (AIC)

value, Akaike’s information criteria (AICc) value corrected for small sample size, difference in

AICc value between best supported model and ith model (ΔAICc), wi (model weight), and ERi

(evidence ratio).

Variable k AIC AICc ΔAICc wi ERi

Date + Date2 + year + Max. Temp

+ Δ Max. Temp +



9 2939 2939.2 0 0.369 1

Date + Date2 + year + Max. Temp

+ Max. Temp*Date + Δ Max.

Temp +



10 2939.1 2939.2 0.086 0.353 1.044

40



central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016.


(Intercept) 1.355 -0.446 3.156

Date -0.875 -1.083 -0.668

Date2 -0.374 -0.535 -0.213

year2016 -0.340 -0.674 -0.006

Max. Temp 0.363 0.126 0.600

Δ Max. Temp -0.118 -0.270 0.034


Δ Binary Precipitation 0.046 -0.275 0.366

Distal Sites -3.243 -5.177 -1.309

41



central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. An asterisk (*)



(Intercept) 1.535 -0.254 3.325

Date -0.287 -0.561 -0.013

Date2 -0.625 -0.793 -0.457

Year 2016 -0.385 -0.702 -0.067

Max. Temp 0.362 0.128 0.597

Δ Max. Temp -0.092 -0.241 0.058









42




indicates an interaction.


(Intercept) 2.588 0.694 4.481

Date -0.726 -0.909 -0.543

Year 2016 -0.869 -1.117 -0.621

Max. Temp 0.846 0.647 1.046

Δ Max. Temp -0.128 -0.252 -0.003

Max Wind -0.139 -0.307 0.029

Δ Max. Wind -0.263 -0.415 -0.112



Distal Sites -3.142 -4.745 -1.539

Date*Max. Temp 0.544 0.382 0.705

43






(Intercept) 2.631 0.720 4.542

Date -0.475 -0.776 -0.174

Year 2016 -0.863 -1.108 -0.618

Max. Temp 0.851 0.651 1.050

Δ Max. Temp -0.129 -0.253 -0.005

Max Wind -0.168 -0.335 0.000

Δ Max. Wind -0.276 -0.427 -0.126






Date*Max. Temp 0.605 0.437 0.773

Date*Distance to Cave1km 0.009 -0.369 0.387



44







(Intercept) 3.624 1.746 5.501

Date -0.216 -0.442 0.010

Date2 -0.003 -0.104 0.099

Date3 0.243 0.141 0.345

Year 2017 -0.010 -0.239 0.219

Avg. Temp 0.904 0.761 1.047

Δ Avg. Temp -0.266 -0.373 -0.158

Avg. Wind -0.507 -0.670 -0.343

Δ Mean. Wind 0.068 -0.064 0.201


Distal Sites -3.384 -5.391 -1.377

Date*Avg. Temp -0.471 -0.591 -0.351

45







(Intercept) 3.629 1.708 5.549

Date -0.195 -0.635 0.245

Date2 -0.194 -0.384 -0.005

Date3 0.110 -0.067 0.287

Year 2017 -0.070 -0.307 0.168

Avg. Temp 0.891 0.743 1.039

Δ Avg. Temp -0.275 -0.387 -0.163

Avg. Wind -0.458 -0.629 -0.288

Δ Mean. Wind 0.075 -0.061 0.212








Date2*Distance to Cave1km 0.293 0.034 0.552






46








(Intercept) 3.769 1.865 5.674

Date -0.120 -0.318 0.078

Date2 -0.045 -0.129 0.040

Date3 0.151 0.066 0.236

Year 2017 -0.282 -0.486 -0.079

Max. Temp 1.011 0.817 1.205

Min. Temp 0.396 0.210 0.581

Δ Max. Temp -0.139 -0.275 -0.003

Δ Min. Temp 0.004 -0.099 0.107

Max Wind -0.448 -0.565 -0.332

Δ Max. Wind -0.072 -0.183 0.040



Distal Sites -2.823 -4.848 -0.799

Date*Max. Temp -0.195 -0.294 -0.095

47








(Intercept) 3.922 2.072 5.773

Date -0.447 -0.849 -0.044

Date2 -0.223 -0.389 -0.056

Date3 0.196 0.034 0.359

Year 2017 -0.242 -0.445 -0.038

Max. Temp 1.001 0.804 1.198

Min. Temp 0.406 0.218 0.594

Δ Min. Temp 0.006 -0.097 0.109

Δ Max. Temp -0.135 -0.270 0.001

Max Wind -0.443 -0.560 -0.326

Δ Max. Wind -0.070 -0.180 0.040







Date*Distance to Cave2km 0.668 0.103 1.234








Date*Max. Temp -0.212 -0.315 -0.108

48




predictors indicates an interaction.


(Intercept) 0.040 -2.346 2.427

Date -0.087 -0.404 0.231

Date2 -0.213 -0.336 -0.089

Date3 0.160 0.033 0.287

Year 2017 -0.440 -0.767 -0.114

Max. Temp 1.501 1.155 1.847

Min. Temp 0.381 0.104 0.657

Δ Max. Temp 0.008 -0.201 0.217

Δ Min. Temp -0.072 -0.223 0.080

Max Wind -0.122 -0.305 0.062

Δ Max. Wind 0.119 -0.068 0.305

Binary Precipitation1 -0.516 -0.819 -0.214


Distal Sites -0.654 -3.184 1.876

Date*Max. Temp -0.077 -0.269 0.115

49




predictors indicates an interaction.


(Intercept) 0.048 -2.212 2.308

Date -0.633 -1.016 -0.249

Date2 -0.220 -0.343 -0.098

Date3 0.185 0.060 0.311

Year 2017 -0.447 -0.773 -0.122

Max. Temp 1.535 1.191 1.878

Min. Temp 0.398 0.116 0.679

Δ Min. Temp -0.058 -0.212 0.095

Δ Max. Temp 0.001 -0.209 0.211

Max Wind -0.075 -0.259 0.110

Δ Max. Wind 0.150 -0.033 0.334

Binary Precipitation1 -0.536 -0.837 -0.236




Distance to Cave3km 0.143 -2.459 2.746




Date*Max. Temp -0.122 -0.312 0.068

50



central Appalachians, Virginia and West Virginia, during spring 2016 and 2017.


(Intercept) 1.012 -0.563 2.587

Date 0.244 0.076 0.412

Year 2017 -0.206 -0.561 0.150

Max. Temp 0.411 0.052 0.769

Min. Temp 0.541 0.228 0.853

Δ Max. Temp -0.261 -0.492 -0.030

Δ Min. Temp 0.047 -0.129 0.223

Max Wind -0.274 -0.467 -0.081

Δ Max. Wind 0.068 -0.130 0.265



Distal Sites -3.091 -4.741 -1.441

51



central Appalachians, Virginia and West Virginia, during spring 2016 and 2017. An asterisk (*)



(Intercept) 0.904 -0.672 2.480

Date 0.065 -0.156 0.286

Date2 -0.052 -0.196 0.092

Year 2017 -0.164 -0.510 0.181

Max. Temp 0.889 0.660 1.118

Δ Max. Temp -0.007 -0.183 0.170









52






(Intercept) 2.837 0.717 4.957

Date -0.470 -0.766 -0.174

Date2 -0.017 -0.150 0.116

Date3 0.344 0.211 0.477

Year 2017 -0.429 -0.729 -0.129

Avg. Temp 0.715 0.525 0.906

Δ Avg. Temp -0.207 -0.344 -0.070

Avg. Wind -0.715 -0.939 -0.491

Δ Mean. Wind -0.007 -0.183 0.169


Distal Sites -3.731 -6.004 -1.458

Date*Avg. Temp -0.581 -0.743 -0.419

53






(Intercept) 2.8309 0.82468 4.83712

Date -0.5601 -0.937 -0.1833

Date2 -0.0269 -0.1612 0.10736

Date3 0.34769 0.21436 0.48101

Year 2017 -0.4052 -0.7103 -0.1

Avg. Temp 0.72747 0.53136 0.92358

Δ Avg. Temp -0.1996 -0.3368 -0.0625

Avg. Wind -0.7066 -0.9311 -0.4821

Δ Mean. Wind -0.007 -0.1826 0.1687








Date*Avg. Temp -0.5733 -0.738 -0.4085

54

Figures

Figure 1-1: Approximate locations of caves acoustic sampling was conducted for bat species during autumn 2015 and 2016

and spring 2016 and 2017; two are located in the Ridge and Valley physiographic sub-province the central Appalachians of

Virginia (A and B), and the third is in the unglaciated central Appalachian Plateau physiographic sub-province of West

Virginia (C).

55

Figure 1-2: Example of acoustic sampling setup around cave sites. Acoustic detector locations were

chosen based on land ownership, access, and proximity to the ‘km rings’, in habitats where Myotis

sodalis (Indiana bat) and Myotis septentrionalis (northern long-eared bat) presence was likely, and

where habitat physical features supported high-quality recordings. Acoustic detectors were

deployed in this manner around three caves in the central Appalachians, Virginia and West

Virginia, during autumn 2015 and 2016 and spring 2016 and 2017.

56

Figure 1-3: Partial effects plot of the interacting relationship between mean daily temperatures, date, and Myotis species’ (Myotis

leibii, eastern small-footed bat; Myotis lucifugus, little brown bat; Myotis septentrionalis, northern long-eared bat; Myotis sodalis,

Indiana bat) echolocation passes per night (with 95% confidence intervals) around three caves in the central Appalachians, Virginia

and West Virginia, during autumn 2015 and 2016. Panels show differences in number of passes between sampling years. Predicted

activity from an early- (blue), mid-(black), and late-season (red) date are shown.

57

Figure 1-4: Partial effects plot of the interacting relationship between mean daily temperatures, date and ‘cavebat’ species’ (Eptesicus

fuscus, big brown bat; Myotis leibii, eastern small-footed bat; Myotis lucifugus, little brown bat; Myotis septentrionalis, northern long-

eared bat; Myotis sodalis, Indiana bat; Perimyotis subflavus, eastern tricolored bat) echolocation passes per night (with 95%

confidence intervals) around three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016.

Panels show differences in number of passes between sampling years. Predicted activity from an early- (blue), mid-(black), and late-

season (red) date are shown.

58

Figure 1-5: Partial effects plot of the relationship between maximum daily temperature and Eptesicus fuscus, big brown bat (EPFU),

echolocation passes per night (with 95% confidence intervals) around three caves in the central Appalachians, Virginia and West

Virginia, during autumn 2015 and 2016.

59

Figure 1-6: Partial effects plot of the interacting relationship between date and distance to hibernacula, and Eptesicus fuscus, big

brown bat (EPFU), echolocation passes per night around three caves in the central Appalachians, Virginia and West Virginia, during

autumn 2015 and 2016. Confidence intervals not shown for clarity.

60

Figure 1-7: Partial effects plot of the relationship between maximum daily temperatures, date and northern long-eared bat, Myotis

septentrionalis, northern long-eared bat (MYSE), echolocation passes per night (with 95% confidence intervals) around three caves in

the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. Panels show differences in number of passes

between sampling years. Predicted activity from an early- (blue), mid-(black), and late-season (red) date are shown.

61

Figure 1-8: Partial effects plot of the relationship between date, distance to hibernacula, and Myotis septentrionalis, northern long-

eared bat (MYSE), echolocation passes per night (with 95% confidence intervals) around three caves in the central Appalachians,

Virginia and West Virginia, during autumn 2015 and 2016.

62

Figure 1-9: Partial effects plot of the interacting relationship between maximum daily temperatures, date, and Myotis sodalis, Indiana

bat (MYSO), echolocation passes per night (with 95% confidence intervals) around three caves in the central Appalachians, Virginia

and West Virginia, during autumn 2015 and 2016. Panels show differences in number of passes between sampling years. Predicted

activity from an early- (blue), mid-(black), and late-season (red) date are shown.

63

Figure 1-10: Partial effects plot of the relationship between date, distance to hibernacula and Myotis sodalis, Indiana bat (MYSO),

echolocation passes per night around three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and

2016. Confidence intervals not shown for clarity.

64

Figure 1-11: Partial effects plot of the relationship between mean daily temperatures, date, and Myotis species’ (Myotis leibii, eastern

small-footed bat; Myotis lucifugus, little brown bat; Myotis septentrionalis, northern long-eared bat; Myotis sodalis, Indiana bat)


Virginia, during spring 2016 and 2017. Predicted activity from an early- (blue), mid-(black), and late-season (red) date are shown.

65

Figure 1-12: Partial effects plot of the relationship between maximum daily temperatures, proximity to caves, and ‘cavebat’ species’

(Eptesicus fuscus, big brown bat; Myotis leibii, eastern small-footed bat; Myotis lucifugus, little brown bat; Myotis septentrionalis,

northern long-eared bat; Myotis sodalis, Indiana bat; Perimyotis subflavus, eastern tricolored bat) echolocation passes per night (with

95% confidence intervals) around three caves in the central Appalachians, Virginia and West Virginia, during spring 2016 and 2017.

Predicted activity from sample sites proximal to cave entrances (blue), and distal sites up to 3 km away from cave entrances (black)

are shown.

66

.

Figure 1-13: Partial effects plot of the relationship between date, distance to cave, and ‘cavebat’ species’ (Eptesicus fuscus, big brown

bat; Myotis leibii, eastern small-footed bat; Myotis lucifugus, little brown bat; Myotis septentrionalis, northern long-eared bat; Myotis

sodalis, Indiana bat; Perimyotis subflavus, eastern tricolored bat) echolocation passes per night around three caves in the central

Appalachians, Virginia and West Virginia, during spring 2016 and 2017.

67

Figure 1-14: Partial effects plots of the relationship between maximum daily temperatures and Eptesicus fuscus, big brown bat

(EPFU), echolocation passes per night (with 95% confidence intervals) around three caves in the central Appalachians, Virginia and

West Virginia, during spring 2016 and 2017.

68

Figure 1-15: Partial effects plot of the interacting relationship between date, distance to cave, and Eptesicus fuscus, big brown bat

(EPFU), echolocation passes per night around three caves in the central Appalachians, Virginia and West Virginia, during spring 2016

and 2017. Confidence intervals not shown for clarity. The EPFU relative activity increased at distal sites faster than at cave sites later

in the spring.

69

Figure 1-16: Partial effects plot of the relationship between minimum daily temperatures, proximity to cave, and Myotis

septentrionalis, northern long-eared bat (MYSE), echolocation passes per night (with 95% confidence interval) around three caves in

the central Appalachians, Virginia and West Virginia, during spring 2016 and 2017. Predicted activity from sample sites proximal to

cave entrances (blue), and distal sites up to 3 km away from cave entrances (black) are shown.

70

Figure 1-17: Partial effects plot of the interactive relationship between date, distance to cave, and Myotis septentrionalis, northern

long-eared bat (MYSE), echolocation passes per night (with 95% confidence intervals) around three caves in the central Appalachians,

Virginia and West Virginia, during spring 2016 and 2017.

71

Figure 1-18: Partial effects plot of the relationship between mean daily temperature, date, and Myotis sodalis, Indiana bat (MYSO),


Virginia, during spring 2016 and 2017. Predicted activity from an early- (blue), mid-(black), and late-season (red) date are shown.

72

Figure 1-19: Partial effects plot of the relationship between mean daily wind speed, proximity to cave, and Myotis sodalis, Indiana bat

(MYSO), echolocation passes per night (with 95% confidence intervals) around three caves in the central Appalachians, Virginia and

West Virginia, during spring 2016 and 2017. Predicted activity from sample sites proximal to cave entrances (blue), and distal sites up

to 3 km away from cave entrances (black) are shown.

73

Figure 1-20: Smoothed trend line of raw data showing general timing of Myotis species’ (Myotis leibii, eastern small-footed bat;

Myotis lucifugus, little brown bat; Myotis septentrionalis, northern long-eared bat; Myotis sodalis, Indiana bat) echolocation passes per

night around three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016.

74

Figure 1-21: Smoothed trend line of raw data showing general timing of ‘cavebat’ species’ (Eptesicus fuscus, big brown bat; Myotis

leibii, eastern small-footed bat; Myotis lucifugus, little brown bat; Myotis septentrionalis, northern long-eared bat; Myotis sodalis,

Indiana bat; Perimyotis subflavus, eastern tricolored bat) echolocation passes per night around three caves in the central Appalachians,

Virginia and West Virginia, during autumn 2015 and 2016.

75

Figure 1-22: Smoothed trend line of raw data showing general timing of Eptesicus fuscus, big brown bat (EPFU), echolocation passes

per night around three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016. Confidence

intervals not shown for clarity.

76

Figure 1-23: Smoothed trend line of raw data showing general timing of Myotis septentrionalis, northern long-eared bat (MYSE),

echolocation passes per night around three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and

2016.

77

Figure 1-24: Smoothed trend line of raw data showing general timing of Myotis sodalis, Indiana bat (MYSO), echolocation passes per

night around three caves in the central Appalachians, Virginia and West Virginia, during autumn 2015 and 2016.

78

Chapter 2: Activity patterns in regional and long-distance migrant bat species during the fall

and spring along ridgelines in the central Appalachians.

Abstract

Although considered a carbon-neutral, clean energy form, wind energy has been shown to have

substantial negative impacts on bat populations, particularly in the Appalachians where high bat

fatality rates are reported. Many central Appalachian ridges offer high wind potential, making

them attractive to future wind energy development. Understanding regional seasonal and hourly

activity patterns of migratory bat species may help to reduce fatalities at existing and future wind

energy facilities and provide guidance for the development of best management practices relative

to bats. To examine hourly migratory bat activity patterns in the fall in Virginia, we acoustically

monitored bat activity on five ridgelines and sideslopes from early September through mid-

November 2015 and 2016 and from early March through late April 2016 and 2017. On ridges,

overall bat activity decreased through the autumn sample period, but was more variable through

the spring sample period. In autumn, migratory bat activity had largely ceased by mid-

November. Activity patterns were species-specific in both autumn and spring sample periods.

Generally, migratory bat activity was related negatively with hourly wind speeds but positively

related with ambient temperatures. These data provide further evidence that operational

mitigation strategies at wind energy facilities would help protect migratory bat species in the

Appalachians; substantially slowing or stopping wind turbine blade spin during periods of low

wind speeds and warm ambient temperatures would help avoid mortality during periods of high

bat activity.

79

Introduction

Although most bat species in eastern North America hibernate in caves during the winter

(hereafter cave bats), the eastern red bat (Lasiurus borealis, LABO), hoary bat (Lasiurus

cinereus; LACI), and silver-haired bat (Lasionycteris noctivagans; LANO) are migratory and

day-roost in trees year round (hereafter tree bats; Barbour and Davis 1969). All three species are

widespread throughout North America during summer months: LABO occur mainly east of the

Continental Divide, LACI generally occur in southern Canada and in northern states, and LANO

occur from southeastern Alaska south throughout Canada and northern United States. (Cryan

2003). Both LANO and LABO appear to overwinter in the southeastern United States; although

less is known of LACI wintering habits, it is assumed the species follows similar migration

patterns, wintering from the southeastern United States as far south as Mexico and Central

America (Kurta 1995). In the central Appalachians, little is known about the specific habits and

activity of tree bats in North America during the spring and fall migration periods, when

mortality from wind energy appears to be greatest (Cryan 2003). The current dearth of

knowledge on tree bats can partly be attributed to the difficulty of studying these highly mobile

species (Holland and Wikelski 2009).

Many studies to date address large-scale seasonal occurrence and distribution patterns,

but smaller-scale, spatially-explicit activity patterns in migratory bats remain largely unexplored

(Cryan 2003, Kunz and Fenton 2006). Furthermore, in the central Appalachians, some LABO

can remain in the region through the winter months, roosting in leaf litter (Davis and Lidicker

1956, Cryan 2003). However, it is unknown if these LABO wintering in the central

Appalachians are resident year-round or seasonal migrants supplying more northern latitudes

80

with their summer residents (Dunbar and Tomasi 2006). A more detailed analysis of autumn and

spring LABO activity patterns in the central Appalachians could help to elucidate overall

seasonal range dynamics.

Migrating bats appear to concentrate along specific travel routes that likely are associated

with specific landscape features, such as mountain ridges, coastlines, and large valleys. For

example in the eastern United States, the Appalachian Mountains and the Atlantic Coast are

physiographic features that may affect and concentrate migratory bat activity (Fiedler 2004,

Kerns and Horn 2005, Baerwald and Barclay 2009, Cryan and Barclay 2009, Hamilton 2012,

Smith and McWilliams 2016). However, for the central Appalachians, it is unknown whether all

or only certain ridges and valleys are important landscape features as is the case for some

migratory birds (Newton et al. 2008), or if patterns are consistent between physiographic

subprovinces within the region (i.e. Blue Ridge, Ridge and Valley, and Allegheny Plateau).

Furthermore, if ridges and/or valleys in the central Appalachians act as migratory corridors, it

remains unclear how elevation of ridges in relation to proximal valley floors (local relief) affects

migratory bat activity.

Most existing migratory tree bat data have come from recent research focusing on

mortality associated with wind turbines (e.g., Arnett et al. 2008, Hayes 2013, Martin et al. 2017).

Extensive increases in wind energy developments across North America and especially in the

Appalachians are expected in the coming decades (AWEA - American Wind Energy

Association, 2016), especially as regional electricity production shifts away from coal-generated

power (McIlmoil and Hansen 2010). Wind energy is the fastest-growing form of energy

production in the U.S. today, and over a decade ago when still in its infancy in the region, it was

81

recognized as a serious threat to bat populations (Tuttle, 2004, Kunz and Fenton 2006, Navigant

Consulting, Inc. 2017).

The vast majority of North American bat-associated wind turbine fatalities are LABO,

LACI, and LANO, when these species are migrating (Fiedler 2004, Kunz and Fenton 2006,

Arnett et al. 2008). There is a considerable degree of variation in bat fatality rates between

different wind farms and between turbine sizes, yet much of this variation remains unexplained

(Barclay et al. 2007). The highest rate of reported fatalities are at wind farms is in the eastern

U.S., specifically in the Appalachians, and wind turbines are located along ridge tops in this

region (Kunz et al. 2007, Arnett et al. 2008, Lott 2008, Hayes 2013). As more wind energy

facilities are developed, it is imperative that land managers understand the relationships between

migratory bats and wind energy in order to develop mitigation practices to curtail severe

mortality events or in some cases potential extinction (Frick et al. 2017).

Clearly, migratory bat activity patterns are variable within seasons, between seasons, and

associated photoperiods and atmospheric conditions (Cryan 2003, Baerwald and Barclay 2011,

Weller and Baldwin 2012, Smith and McWilliams 2016). Autumn migratory bat activity patterns

appear to be positively related with storm front passage and associated ambient conditions,

resulting in higher mortality rates (Arnett et al. 2008, Baerwald and Barclay 2011, Smith and

McWilliams 2016). Indeed, mortality rates among migratory bat species are higher in autumn

compared with all other seasons (Arnett et al. 2008, Cryan and Barclay 2009). Lower mortality

rates at wind turbines during spring and summer, compared with autumn, suggest that there exist

important differences in migration behavior between seasons (Arnett et al. 2008, Grodsky et al.

2012). In eastern North America, patterns of migration in bats may be somewhat comparable to

terrestrial bird migration, whereby different migratory pathways between seasons has been

82

observed (La Sorte et al. 2014). Furthermore, these differences between seasons are influenced

by differences in seasonal atmospheric conditions.

Numerous studies have documented a positive relationship between ambient

temperatures and activity of migratory tree bats during the spring and autumn, with activity

generally increasing above 10 ºC (Fiedler 2004, Kerns and Horn 2005, Baerwald and Barclay

2011, Weller and Baldwin 2012, Bender and Hartman 2015, Smith and McWilliams 2016,

Dechmann et al. 2017). However, research on the effect of barometric pressure on migratory bat

activity has been less conclusive. Cryan and Brown (2007), Baerwald and Barclay (2011), and

Dechmann et al. (2017) all observed lower and dropping barometric pressures were correlated

with increased activity, whereas others have found the opposite (Bender and Hartman 2015,

Smith and McWilliams 2016). Indeed, barometric pressure may affect migratory bat activity

positively and negatively: higher pressures are associated with easier flight conditions while

dropping pressure may indicate a passing storm front (Richardson 1978, Smith and McWilliams

2016). Precipitation negatively affects migratory bat activity, likely due to a combination of

attenuation affecting echolocation, exposure on an individual level, associated atmospheric

conditions, and influence on insect prey (Griffin 1971, Arnett et al. 2007, Lacki et al. 2007).

However, the effects of precipitation on bat activity may be negligible when compared to other

atmospheric conditions. For example, Smith and McWilliams (2016) found that autumn

migratory activity of bats in Rhode Island was less influenced by precipitation than by

temperature, wind direction, and changes in pressure.

Landscape feature relief and elevation likely affects migratory bats differently between

seasons and among species, and may depend on “pre-determined” migratory pathways (Reynolds

2006, Baerwald and Barclay 2009). Additionally, geographic conditions, especially elevation,

83

greatly influences atmospheric conditions in the Appalachians (Lindberg et al. 1988). Average

temperatures are lower whereas precipitation amount and duration are greater at higher

elevations (Lindberg et al. 1988). Furthermore, more precipitation occurs on windward slopes in

the Appalachians, and because many weather fronts travel from west to east, the Allegheny

physiographic sub-province is generally more mesic than the Ridge and Valley (Ray 1986, Soulé

1998). Among these atmospheric and geographic conditions, it is clear that ambient temperatures

affect migratory bat activity, and temperature likely interacts with other conditions such as wind

speed, wind direction, and elevation. Notably, Wolbert et al. (2014) found a significant

interaction between the effects of relative elevation and ambient temperatures on bat activity,

such that ambient temperatures had greater effects on bat activity at higher elevations.

Wind speeds are generally greater at higher elevations in the Appalachians (Lindberg et

al. 1988). However, bat mortality at wind turbines has been associated with lower wind speeds

(Fiedler 2004, Kerns and Horn 2005, Arnett et al. 2008, Baerwald and Barclay 2011). The cut-in

wind speed, whereby turbines begin to produce energy, is typically between 11 and 14.5 kph,

whereas the rated wind speed (where maximum electricity is generated) is around 40 to 56 kph.

Most bat mortality occurs around and below cut-in wind speed, when energy production is less

than optimal (Fiedler 2004, Arnett et al. 2011, Martin et al. 2017). Furthermore, greater bat

activity is associated with lower wind speeds regardless of geographic location or position

(Cryan and Brown 2007, Smith and McWilliams 2016, Dechmann et al. 2017). Additionally,

wind direction may also influence migratory bat activity, in both the fall and spring migration

periods, with tailwinds associated with increased bat activity (Smith and McWilliams 2016,

Dechmann et al. 2017). Wind direction and wind speed likely have interactive effects on

migratory bat activity (Smith and McWilliams 2016, Dechmann et al. 2017).

84

Recent studies suggest that simply raising turbine cut-in speed is an effective strategy to

reduce a high proportion of bat mortality at wind facilities, while still maintaining economic

viability (Baerwald et al. 2009, Arnett et al. 2011, Weller and Baldwin 2012, Arnett et al. 2013,

Martin et al. 2017). Greater, site-specific understanding of the effect of wind speed and wind

direction on migratory behavior could contribute to management practices that greatly reduce

future wind energy-associated bat mortality. Identification of important migratory pathways, and

geographic features associated with them could significantly reduce bat mortality associated with

wind turbines through development of better, site and geography-specific curtailment strategies.

My objectives were to determine major activity patterns and define drivers thereof for

migratory bat species during the autumn and spring migration periods along ridgelines and

sideslopes in the central Appalachians, an area where wind energy development is increasing. I

expected activity patterns to vary among migratory species both temporally and spatially. During

the fall migratory period, I anticipated greater migratory bat activity at higher elevations, but

greater activity at lower elevations during the spring migratory period. Furthermore, I expected

that ambient conditions influence migratory bat activity during the fall and spring migratory

periods in the central Appalachians. Regardless of season, I expected decreased levels of activity

during periods of high wind speed, low temperatures, and during bouts of precipitation.

Methods

Study Area

I conducted my study on five mountain massifs (ridges) and adjacent sideslopes in the

Ridge and Valley and Blue Ridge sub-provinces of the central Appalachian Mountains in

Virginia (Figure 2-1). I chose these five ridges because the maximum elevations were over

85

1000m, were areas denoted with high wind potential and were topographically similar to many

wind farms in the mountains of the eastern United States (Virginia Center for Wind Energy

2018). These areas included: Sugar Run Mountain (Giles County, elevation 1,238 m), Salt Pond

Mountain (Giles County, elevation 1,329 m), Back Creek Mountain (Bath County, elevation

1,102 m), Big Flat Mountain (Rockingham County, elevation 1,033 m), and Hazeltop Mountain

(Madison County, elevation 1,162 m). Study sites were located within Jefferson and George

Washington National Forest lands or Shenandoah National Park. The forests types throughout

are generally xeric-to moderately mesic oak associations on ridges with mixed mesophytic forest

along drainages or sheltered north-facing slopes (Braun 1950). Multiple species of oak occur,

with white oak (Quercus alba) and chestnut oak (Quercus pinus) being dominant. In lower

elevations and along riparian corridors, mesic species such as eastern white pine (Pinus strobus),

tulip poplar (Liriodendron tulipifera), and eastern hemlock (Tsuga canadensis) are common

(Kniowski 2016).

Data Collection

I collected acoustic data using Song Meter ZC detectors with SMM-U1 microphones,

Song Meter SM2 detectors with SMX-U1 microphones, and Song Meter SM4 detectors with

SMM-U1 microphones (Wildlife Acoustics, Maynard, MA) from early September to mid-

November in 2015 and 2016, when migratory tree bats depart summer ranges and fly south, and

in early March to late April in 2016 and 2017, when bats migrate northwards and disperse across

the landscape to summer ranges (Cryan 2003, Baerwald and Barclay 2011).

I deployed detectors longitudinally along ridgelines, with three detectors placed at high

elevations (higher than 1000m where possible), one detector at mid-elevation (roughly halfway

between the ridgetop elevation and the associated valley floor elevation, 500-750m), and one

86

detector at low elevation (generally less than 300 meters elevation and proximal to a valley,

Figure 2-2). I used digital elevation models in ArcMap 10.3.1® (ESRI, Redlands, CA) software

to derive elevation, and choose potential general detector locations at each elevation category. I

programmed detectors to record nightly from 1900 to 0700 hours, and precise detector locations

were chosen based on accessibility, likelihood of migratory bat presence, and site characteristics

known to produce high-quality call recordings (i.e., low clutter such as a forest canopy

gap/riparian corridor).

I collected hourly weather data from the airport nearest to each detector site

(https://www.wunderground.com/ 2017). I created a wind profit variable by combining average

wind speeds and average hourly wind directions, and used it to test if bats use tailwinds for

advantageous flight during migratory seasons (Smith and McWilliams 2016). Because bats to

use the linear arrangement of ridgelines to migrate north or south depending on season,I assumed

that winds running parallel to ridgelines would have either a positive or negative effect on bat

activity. In autumn, when average hourly winds are predominantly from the southwest, I

assigned wind profit a negative value, whereas wind predominantly from the northeast incurred a

positive wind value. I assigned the of inverse autumn wind profit to the spring season to account

for opposite migration patterns of bats.

I identified acoustic call data to species using Kaleidoscope version 4.3.1 (Wildlife

Acoustics, Maynard, Massachusetts), classifier 4.2.0 at the neutral setting, with default signal

parameters (8-120 KHz frequency range, 500 maximum inter-syllable gap, two minimum

number of pulses, enhanced with advanced signal processing, (USFWS: Indiana Bat Summer

Survey Guidance - Automated Acoustic Bat ID Software Programs, Wildlife Acoustics -

Overview of Kaleidoscope Pro 3 Analysis Software). I limited subsequent analyses to include

87

only call data with a minimum of three call pulses, to reduce automated species identification

errors. I manually reviewed recorded files using program AnalookW version 4.1t (Titley

Electronics, Columbia, MO) to validate automated species identification and check for

systematic and systemic errors (Lemen et al. 2015).


I created a set of a priori candidate generalized additive mixed models (GAMMs)

representing specific hypotheses about the relationship between atmospheric and habitat

variables and hourly bat activity. I used the same variables and models for analyses of both fall

and spring data. Candidate models included additive and interactive combinations of date, hour,

site, landscape characteristics, and ambient conditions (Table 2-1, Table 2-2). I assessed

multicollinearity among predictors using package ‘corrplot’ (Wei and Simko 2016) in program R

version 3.2.3 (R Core Team 2013) to ensure highly correlated variables were not included within

the same model. I tested for serial correlation in hourly bat activity, for each species, using R

package ‘stats’. I fit all generalized additive mixed models using the GAMM function from R

package ‘mgcv’, with an autoregressive random effects structure to account for the serial

correlation of bat echolocation passes between hours at any given site (hour nested within each

unique site-date), and with a negative binomial link function to account for overdispersion in bat

pass counts (Wood 2017). I used generalized additive mixed models because migratory bat

activity may display nonlinear responses to independent variables, and these models allow for

curvilinear responses. I used an information theoretic approach to select the best supported

model, ranking models using Akaike’s Information Criterion corrected for small sample size

(AICc from package MuMIn, Bartoń 2015; Burnham and Anderson 2002). I centered and scaled

all continuous predictors to aid model fitting and to facilitate assessment of main effects of

88

interactions (Schielzeth 2010). I modeled activity of each species individually, and for the

combined activity of all three species to assess potential differences among species as well as

drivers of general bat activity in autumn and spring, regardless of year (Arnett et al. 2016).

Results

I sampled a total 183 site-nights during autumn 2015 and 2016, and a total 109 site-nights

during spring 2016 and 2017. Due to detector failure, and inaccessibility due to weather, some

detector recordings at specific sites sometimes were not continuous over the two years.

Kaleidoscope identified 3,322 and 5,118 LANO acoustic passes, 2,657 and 2,512 LABO

acoustic passes, and 2,051 and 892 LACI acoustic passes in data recorded during autumn 2015

and 2016, respectively. Kaleidoscope identified 3,178 and 10,851 LANO acoustic passes, 1,409

and 1,413 LABO acoustic passes, and 1,913 and 5,243 LACI acoustic passes in data recorded

during spring 2016 and 2017, respectively.

Autumn activity patterns

The best supported model describing total migrant hourly activity contained mean hourly

temperature, date, hour of sampling, change in temperature from 4 hours prior, mean hourly

barometric pressure, mean hourly wind speed, change in barometric pressure from 6 hours prior,

and mean hourly wind profit (in order of decreasing effect sizes; Table 2-3). The best supported

model also contained hourly precipitation (presence or absence per hour) and relative elevation

(categorical variables; Table 2-3). All variables except hourly wind profit had non-zero effects.

No other models had empirical support (ΔAICc<2). Smoothers were supported for temperature,

date, and hour (Table 2-3). All variables except mean hourly wind profit displayed non-zero

effects. Activity decreased over the season with a pulse of activity in October (Figure 2-3).

Activity was positively related to mean hourly temperature and negatively related to hour of

89

sampling (Figure 2-4 and Figure 2-5). Among the remaining continuous predictors, barometric

pressure, wind speed, and change in temperature from 4 hours prior had the largest effect sizes

(Table 2-3). Migrants were more active at higher barometric pressures, lower wind speeds, and

when temperatures decreased more from 4 hours prior (Figure 2-6 and Figure 2-7). Although

contained in the best supported model, mean hourly wind speed, mean hourly wind profit, mean

hourly barometric pressure, relative elevation, change in temperature from 4 hours prior, and

change in barometric pressure from 6 hours prior had minimal effect sizes.

The best supported model describing autumn LANO hourly activity contained Julian

date, mean hourly temperature, hour of sampling, change in temperature from 4 hours prior,

mean hourly barometric pressure, change in barometric pressure from 6 hours prior, and mean

hourly wind speed (In order of decreasing effect sizes; Table 2-4). The model also included

relative elevation (categorical). No other models were competing. Smoothers were supported for

temperature, date, and hour (Table 2-4). All variables except relative low elevations displayed

non-zero effects. Activity levels were variable over the season with a pulse of activity in

October, were negatively related to hour of sampling, and were positively related to mean hourly

temperature, peaking around 20 degrees (Celsius; Figure 2-8, 2-9, and 2-10, respectively).

Among other continuous variables, change in temperature from 4 hours prior had the largest

effect size, and activity was greater when the temperature decreased more (Figure 2-11). Activity

was greatest at low elevations and lesser at higher elevations, but confidence intervals

overlapped to a large extent. Increased activity was related marginally to barometric pressure and

change in pressure from 6 hours prior, but negatively related to wind speed.

The best supported model describing LABO hourly activity including hour of sampling,

mean hourly wind speed, change in barometric pressure from 6 hours prior, and mean hourly

90

wind profit (In order of decreasing effect sizes; Table 2-5). The model also included hourly

binary precipitation (categorical). No other models were competing (ΔAICc<2). The smoother on

hour was supported, and LABO activity was higher in the first few and last few hours of the

night (Figure 2-12). All variables except mean hourly wind profit displayed non-zero effects.

Among remaining continuous predictors, wind speed had the largest effect size (Table 2-5).

Activity of LABO was related negatively with wind speed, and activity was negatively related to

precipitation (Figure 2-13). Although also contained in the best supported model, wind profit and

change in barometric pressure from 6 hours prior had minimal effect sizes.

I was unable to model autumn LACI activity patterns due to limited nightly echolocation

passes recorded. Kaleidoscope identified 2,943 LACI acoustic passes in data recorded over both

autumn sampling periods (2015 and 2016), but only 892 LACI acoustic passes (~5 passes per

night/~0.4 passes per hour) were recorded during the autumn sampling period in 2016.

Spring Activity Patterns

The best supported model describing migrants’ hourly activity contained Julian date,

hour, mean hourly temperature, mean hourly wind speed, change in barometric pressure from 6

hours prior, and mean hourly wind profit (in order of decreasing effect sizes; Table 2-6). The

model also included hourly binary precipitation (categorical). No other models were competing.

Smoothers were supported for both hour and date (Table 2-6). All variables except mean hourly

wind profit displayed non-zero effects. Activity generally increased over the season, with a peak

in mid-April, and activity was higher in the first few hours of the night (Figure 2-14 and Figure

2-15). Among other continuous predictors, temperature had the largest effect size, and activity

was related positively with temperature (Figure 2-16). Although contained in the best supported

model, mean hourly wind speed had only a marginally negative effect on activity, as did change

91

in pressure from 6 hours prior. Activity also was related negatively to hourly binary

precipitation.

The best supported model describing LANO hourly activity contained Julian date, hour,

mean hourly temperature, mean hourly wind speed, change in barometric pressure from 6 hours

prior, and mean hourly wind profit (in order of decreasing effect sizes; Table 2-7). The model

also included hourly binary precipitation (categorical). No other models were competing.

Smoothers were supported for both hour and date (Table 2-7). All variables except mean hourly

wind profit displayed non-zero effects. Activity generally increased over the season, with a peak

in mid-April, and activity was higher in the first few hours of the night (Figure 2-17 and Figure

2-18). Activity was related negatively to hourly precipitation. Spring LANO activity was related

positively to temperature, and temperature had a large effect size (Figure 2-19). Although

contained in the best supported model, mean hourly wind speed had only a marginally negative

effect on activity (Figure 2-20), as did change in pressure from 6 hours prior.

The best supported model describing LABO hourly activity contained continuous

variables including Julian date, hour, mean hourly temperature, mean hourly wind speed, change

in barometric pressure from 6 hours prior, and mean hourly wind profit (in order of decreasing

effect sizes (Table 2-8). The model also included hourly binary precipitation (categorical). No

other models were competing. Among smoothed terms, both hour and date had well supported

smoothers (Table 2-8). All variables except mean hourly wind profit displayed non-zero effects.

Activity decreased slightly over the season, and activity peaked during the 3rd and 8th hour of

sampled nights (Figure 2-21 and Figure 2-22). Among other continuous predictors, temperature

and wind speed had the largest effect sizes. Activity was related positively with temperature, but

negatively related with wind speed and hourly binary precipitation (Figure 2-23 and Figure 2-

92

24). Although also included in the best supported model, wind profit and change in barometric

pressure from 6 hours prior had minimal effect sizes.

The best supported model describing LACI hourly activity contained Julian date, hour of

sampling, mean hourly temperature, and an interaction between date and mean hourly

temperature (in order of decreasing effect sizes; Table 2-9). The model also included hourly

binary precipitation (categorical). No other models were competing. Smoothers were supported

for both hour and date (Table 2-9). All variables except hourly binary precipitation had non-zero

effects. Activity generally increased over the season, with a peak in mid-April, and activity was

higher in the first few hours of the night (Figure 2-25 and Figure 2-26). Activity was related

negatively to hourly precipitation. Among other continuous predictors, mean hourly temperature

had the largest effect size, and activity was related positively to temperature (Figure 2-27). The

interaction between date and temperature had minimal effect size.

Discussion

Migratory bat activity was related closely to temporal (date) and weather variables,

notably hourly wind speed, in both autumn and spring. However, the relationship of autumn and

spring activity to other environmental conditions varied among migratory bat species. My results

suggest wind speed may be the best overall predictor of migratory bat activity in the central

Appalachians. Corroborating previous research from other geographic regions, my data suggest

that migratory bat species are more active at lower wind speeds, as is the observed case

throughout these species’ ranges (Fiedler 2004, Reynolds 2006, Baerwald and Barclay 2011,

Weller and Baldwin 2012).

The majority of migratory bat activity occurred at winds speeds lower than the 11 to 14.5

kph cut-in wind speed common for most industrial wind turbines (Fiedler 2004). Wind turbine

93

blades often continue to free-spin below cut-in wind speeds and are capable of causing bat

mortality at these speeds (Arnett et al. 2013). Bat mortality rates can be significantly reduced by

raising cut-in speeds for blade unlocking and/or at minimum, directionally feathering blades to

reduce blade speed if below production cut-in wind levels (Arnett et al. 2011, 2013). Mortality

reduction rates vary amongst operational mitigation experiments, but 50% or greater reduction in

bat mortality is commonly achieved (Arnett et al. 2013). Projections for the economic costs of

operational mitigation and annual output lost vary greatly due to differences in exact mitigation

treatment implemented (i.e. seasonal duration), associated turbine technology/limitations, and

geographic locations/wind patterns (Arnett et al. 2013). However, wind speeds are not the only

predictor of migratory bat activity, and my results suggest that migratory bats appear to respond

to atmospheric conditions differently based on geographic region. Using a combination of

conditions to inform operational mitigation at wind energy facilities may further reduce

migratory bat fatality rates while optimizing energy production. Accordingly, our findings could

inform energy producers and regulators about potential migratory bat mortality factors to aid in

the development or modification of industry best management practices (BMP) relative to bats

(Frick et al. 2017).

Previous research has found a definitive positive relationship between migratory bat

species’ activity and ambient temperatures, regardless of season, and my results largely follow a

similar pattern (Reynolds 2006, Bender and Hartman 2015, Smith and McWilliams 2016,

Bernard and McCracken 2017). In general, hourly temperatures and date had the large effect on

overall bat activity during the autumn and spring migration periods. Moreover, hourly

precipitation typically suppressed migratory bat activity during the autumn and spring (Griffin

1971, Smith and McWilliams 2016). However, some differences existed between species’ and

94

species’ groups’ relationship to environmental conditions and intra-seasonality, supporting prior

research (Baerwald and Barclay 2011).

Autumn Activity

Date was not related closely to autumn LABO activity in my study, but in Rhode Island,

Smith and McWilliams (2016) found evidence to the contrary. However, Rhode Island is well

outside of LABO wintering grounds (Cryan 2003). It is possible that autumn LABO activity in

this study was not related to date because sites were within the species northernmost wintering

range (Davis and Lidicker 1956, Cryan 2003) (Figure 2-28). Thus, recorded LABO activity

potentially could be attributable to resident individuals throughout the autumn sampling period,

rather than to migrating individuals. Furthermore, Bernard and McCraken (2017) found

migratory bat species were active throughout the winter months in Tennessee, across a range of

ambient temperatures; similarly, temperature did not substantially affect autumn LABO activity

at my study sites. Depending on ambient conditions, LABO likely restricted activity due to

metabolic/thermal tradeoffs balancing insect prey availability with energy expenditures

associated with active foraging behavior (Bender and Hartman 2015, Bernard and McCracken

2017). Unsurprisingly, LABO activity was lower during hours with any precipitation, likely due

to reducing foraging efficiency with decreased insect activity and increasing metabolic costs

associated with flight and exposure (Griffin 1971, Voigt et al. 2011).

Although date and hourly ambient temperatures appeared to have little effect on autumn

LABO activity, they remained important predictors of activity for autumn LANO and grouped

migrant species. The observed peak of autumn LANO activity in mid-October may correspond to

a final migratory push or “wave” as suggested by McGuire et al. (2012). Furthermore, LANO,

LACI, and all migrant species activity was positively related to mean hourly temperature, likely

95

due to insect prey availability and metabolic costs of activity and/or migration in cold

temperatures (Reynolds 2006, McGuire et al. 2014, Wolbert et al. 2014).

My results generally corroborate previous research on the seasonal distribution and

migratory timing of migratory bat species (Cryan 2003, Johnson et al. 2003). The majority of

migratory bat activity in the central Appalachians occurs somewhat later in the year compared to

more northern regions where most activity (and hence wind-related mortality) occurs in August

and early September (Johnson et al. 2003, Baerwald and Barclay 2011, McGuire et al. 2012,

Arnett et al. 2016). In Ontario, Canada, a final autumn LANO migration “wave” occurred in

mid-September (McGuire et al. 2012), nearly four weeks earlier than the final “wave” I observed

in the central Appalachians. Geographic variation in the timing of major migratory pulses also

has important implications regarding future wind energy development and potential bat mortality

mitigation policies in the central Appalachians. Understanding timing of major “waves” of

activity at multiple regions also could provide data inputs to managers to further adjust or modify

wind energy BMPs.

Contrary to my expectations, overall autumn migratory bat activity was related negatively

to relative elevation. Migratory bat species may use the linear arrangement of the Ridge and

Valley and Blue Ridge sub-provinces to navigate during autumn migration in the central

Appalachians (Baerwald and Barclay 2009, Furmankiewicz and Kucharska 2009), but my results

provide evidence that valleys are the preferred migratory corridors in this region, rather than

ridgetops. In the central Appalachians of Virginia, valleys have a higher prevalence of cleared

habitats such as pasture whereas ridgetops typically are forested (Kniowski and Ford 2017).

Various bat species utilize linear landscape features such as riparian areas for foraging and

movement between foraging/roosting areas (Limpens and Kapteyn 1991, Ford et al. 2005), and

96

migratory bats in the central Appalachians may be more active in areas with these features due to

proximity to roosting habitat, and/or improved migrating and foraging conditions due to lack of

environmental clutter (Brigham et al. 1997, Kunz et al. 2007, Drake et al. 2012, Wolbert et al.

2014, Brooks et al. 2017).

I found little evidence that wind profit substantially affects autumn migratory species’

activity patterns in the central Appalachians, contrasting with previous research in Rhode Island

(Smith and McWilliams 2016). However, Smith and McWilliams’ (2016) acoustic detector sites

were generally far from potential roosting habitat (1-2 km), whereas my detectors generally were

surrounded by mature hardwood forest suitable for day-roost use. It is possible that bat activity is

more affected by wind direction in open habitats than in heavily forested habitats that alter wind

dynamics (Quine and Gardiner 2007).

Although the general seasonality of bat migration are known, patterns of bat activity on

an hourly basis are less understood (Kunz 1973, Baerwald and Barclay 2011). Hour (of

sampling) each night generally had a negative effect on overall migratory bat activity during

autumn, with most activity occurring within the first few hours of night, similar to previous

findings (Kunz 1973). However, LABO displayed a unimodal response to hour of nightly

sampling in autumn, suggesting these bats are likely foraging in the first hours after sunset, and

the last few hours prior to sunrise (Kunz 1973, Kunz and Lumsden 2003, Kunz 2013). Eastern

red bats are known to forage early in the evening (sometimes before sunset), and may be active

at slightly different times of day due to differences in prey availability, day-roost selection and

proximity to foraging areas, or even interspecific temporal niche partitioning (Kunz 1973).

Spring Activity

97

Spring migratory bat activity was related to date, and ambient temperature was an

important driver of activity for all migratory species and species groups in the spring season

(Reynolds 2006, Bender and Hartman 2015, Smith and McWilliams 2016, Bernard and

McCracken 2017). Similar to autumn migratory bat activity, spring activity is related positively

with ambient temperature likely due to insect prey availability and metabolic costs of

migration/flight in cold temperatures (Reynolds 2006, McGuire et al. 2014, Wolbert et al. 2014).

Overall migratory bat activity increased through the spring sample season, but displayed defined

peaks of activity, again suggesting that bats may migrate in ‘waves’ during the spring months,

similar to autumn activity. These spring ‘waves’ also may be correlated with ambient conditions,

such as temperatures. Activity of all migratory species grouped and LACI and LANO

individually peaked in mid-April in the central Appalachians of Virginia. However, spring

activity of LABO decreased slightly through time, in contrast to what I expected. These data may

suggest that LABO wintering in the central Appalachians may migrate (likely northward) in

spring, and a subsequent “wave” of LABO had yet to arrive prior to late April. Pulses or “waves”

of activity in the spring season may be caused by differences in migration timing between sexes,

or differences in wintering grounds between sexes (Cryan 2003, Cryan and Wolf 2003, Jonasson

and Guglielmo 2016). Similar timing and patterning of activity has been observed north of my

study area (Cryan 2003, Reynolds 2006, Grodsky et al. 2012).

Contrary to my expectations, elevation was not included within the best supported model

describing spring activity for any migratory species or species group. Greater recorded mortality

rates during fall migration at wind energy sites suggest migratory bats use higher

elevations/ridgelines more during the fall and lower elevations/valleys during spring migration

(Johnson et al. 2003, Reynolds 2006, Grodsky et al. 2012). My results indicate migratory bat

98

activity is more evenly dispersed across the landscape in the spring compared with autumn

(Johnson et al. 2003). Sex and reproductive condition may contribute to different migration

patterns between autumn and spring as suggested by Jonasson and Guglielmo (2016). Female

migratory bats leave wintering grounds earlier than males (Cryan 2003), and likely use torpor

less than males during the spring due to pregnancy (Ford et al. 2002, Turbill and Geiser 2006,

Dzal and Brigham 2013). Without extensive use of torpor during spring migration, female

migratory bats need to forage more to cope with thermoregulatory costs incurred by low ambient

temperatures (Ford et al. 2002, Cryan and Wolf 2003, Jonasson and Guglielmo 2016). Increased

foraging behavior of female migratory bats during spring migration compared to autumn

migration may explain why my results suggest more widespread migratory bat activity on the

landscape, with relative elevation having little influence on activity.

Wind speed was not included in the best supported model describing spring LACI

activity, suggesting other ambient conditions may have greater influence on their behavior,

possibly due to larger bodies and more powerful flight compared to other migratory species

(Barbour and Davis 1969, Salcedo et al. 1995, Riskin et al. 2010). However, LACI may be

affected by wind speeds, but the relationship could be difficult to identify because data were

limited. For instance, actively migrating bats may fly outside the range of detection (higher

and/or during different atmospheric conditions) or that they do not forage during migration

flight.

Due to the nature of acoustic data, it is not possible to distinguish between a bat that is

foraging and a bat that is migrating. I assumed that more acoustic activity inherently was related

to greater numbers of migrating bats, but this actual relationship is untested. Previous research in

Illinois has suggested that actively migrating bats may occasionally use visual cues as an

99

alternative to echolocation therefore less likely to be detected during acoustic surveys (Timm

1989). Therefore, my results may be underestimating the number of bat passes at my sampled

sites. However, in Europe, migratory bat species use echolocation during migration periods

(Furmankiewicz and Kucharska 2009), therefore my acoustic detectors likely recorded such

activity of bats that included migratory activity (Cryan et al. 2014, Smith and McWilliams 2016).

A detailed, regional understanding of the effects of atmospheric conditions, date, and

landscape features on migratory bat activity will allow land managers to better assess the risk of

bat mortality at future wind energy development sites, as well as offering potential pathways to

maximize power output while reducing bat mortality at existing wind energy sites. Further

elucidating migratory bat activity patterns, especially in autumn, could influence future wind

turbine operational mitigation regimens, allowing for both maximum power generation while

minimizing bat mortality (Arnett et al. 2013, Martin et al. 2017).

100

Literature Cited

Arnett, E. B., E. F. Baerwald, F. Mathews, L. Rodrigues, A. Rodríguez-Durán, J. Rydell, R.

Villegas-Patraca, and C. C. Voigt. 2016. Impacts of wind energy development on bats: A

global perspective. Pages 295–323 in. Bats in the Anthropocene: Conservation of Bats in

a Changing World. Springer, Cham.

Arnett, E. B., W. K. Brown, W. P. Erickson, J. K. Fiedler, B. L. Hamilton, T. H. Henry, A. Jain,

G. D. Johnson, J. Kerns, R. R. Koford, C. P. Nicholson, T. J. O’connell, M. D.

Piorkowski, and R. D. Tankersley. 2008. Patterns of bat fatalities at wind energy facilities

in North America. The Journal of Wildlife Management 72:61–78.

Arnett, E. B., M. M. Huso, D. S. Reynolds, and M. Schirmacher. 2007. Patterns of pre-

construction bat activity at a proposed wind facility in northwest Massachusetts. An

annual report submitted to the Bats and Wind Energy Cooperative. Bat Conservation

International. Austin, Texas, USA.

Arnett, E. B., M. M. Huso, M. R. Schirmacher, and J. P. Hayes. 2011. Altering turbine speed

reduces bat mortality at wind-energy facilities. Frontiers in Ecology and the Environment

9:209–214.

Arnett, E. B., G. D. Johnson, W. P. Erickson, and C. D. Hein. 2013. A synthesis of operational

mitigation studies to reduce bat fatalities at wind energy facilities in North America. A

report submitted to the National Renewable Energy Laboratory. Bat Conservation

International. Austin, Texas, USA.

AWEA - American Wind Energy Association. http://www.awea.org/. Accessed 9 May 2016.

Baerwald, E. F., and R. M. R. Barclay. 2009. Geographic variation in activity and fatality of

migratory bats at wind energy facilities. Journal of Mammalogy 90:1341–1349.

Baerwald, E. F., and R. M. R. Barclay. 2011. Patterns of activity and fatality of migratory bats at

a wind energy facility in Alberta, Canada. The Journal of Wildlife Management 75:1103–

1114.

Baerwald, E. F., J. Edworthy, M. Holder, and R. M. R. Barclay. 2009. A large-scale mitigation

experiment to reduce bat fatalities at wind energy facilities. Journal of Wildlife

Management 73:1077–1081.

Barbour, R. W., and W. H. Davis. 1969. Bats of America. Volume 7. University Press of

Kentucky Lexington.

Barclay, R. M. R., E. F. Baerwald, and J. C. Gruver. 2007. Variation in bat and bird fatalities at

wind energy facilities: assessing the effects of rotor size and tower height. Canadian

Journal of Zoology 85:381–387.

Bartoń, K. A. 2015. Package “MuMIn” | Multi-Model Inference.

101

Bender, M. J., and G. D. Hartman. 2015. Bat activity increases with barometric pressure and

temperature during autumn in central Georgia. Southeastern Naturalist 14:231–242.

Bernard, R. F., and G. F. McCracken. 2017. Winter behavior of bats and the progression of

white-nose syndrome in the southeastern United States. Ecology and Evolution 7:1487–

1496.



Brigham, R. M., S. D. Grindal, M. C. Firman, and J. L. Morissette. 1997. The influence of

structural clutter on activity patterns of insectivorous bats. Canadian Journal of Zoology

75:131–136.

Brooks, J. D., S. C. Loeb, and P. D. Gerard. 2017. Effect of forest opening characteristics, prey

abundance, and environmental factors on bat activity in the Southern Appalachians.

Forest Ecology and Management 400:19–27.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a

practical information-theoretic approach. Springer, New York.

Cryan, P. M. 2003. Seasonal distribution of migratory tree bats (Lasiurus and Lasionycteris) in

North America. Journal of Mammalogy 84:579–593.

Cryan, P. M., and R. M. R. Barclay. 2009. Causes of bat fatalities at wind turbines: hypotheses

and predictions. Journal of Mammalogy 90:1330–1340.

Cryan, P. M., and A. C. Brown. 2007. Migration of bats past a remote island offers clues toward

the problem of bat fatalities at wind turbines. Biological Conservation 139:1–11.

Cryan, P. M., P. M. Gorresen, C. D. Hein, M. R. Schirmacher, R. H. Diehl, M. M. Huso, D. T. S.

Hayman, P. D. Fricker, F. J. Bonaccorso, D. H. Johnson, K. Heist, and D. C. Dalton.

2014. Behavior of bats at wind turbines. Proceedings of the National Academy of

Sciences of the United States of America 111:15126–15131.

Cryan, P. M., and B. O. Wolf. 2003. Sex differences in the thermoregulation and evaporative

water loss of a heterothermic bat, Lasiurus cinereus, during its spring migration. The

Journal of Experimental Biology 206:3381–3390.

Davis, W. H., and W. Z. Lidicker. 1956. Winter range of the red bat, Lasiurus borealis. Journal

of Mammalogy 37:280–281.

Dechmann, D. K. N., M. Wikelski, D. Ellis-Soto, K. Safi, and M. T. O’Mara. 2017.

Determinants of spring migration departure decision in a bat. Biology Letters

13:20170395.

102

Drake, D., S. Schumacher, and M. Sponsler. 2012. Regional analysis of wind turbine-caused bat

and bird fatality. Environmental and Economic Research and Development Program of

Wisconsin’s Focus on Energy, Madison, Wisconsin, USA.

Dunbar, M. B., and T. E. Tomasi. 2006. Arousal patterns, metabolic rate, and an energy budget

of eastern red bats (Lasiurus borealis) in winter. Journal of Mammalogy 87:1096–1102.

Dzal, Y. A., and R. M. Brigham. 2013. The tradeoff between torpor use and reproduction in little

brown bats (Myotis lucifugus). Journal of Comparative Physiology B 183:279–288.

Fiedler, J. K. Assessment of bat mortality and activity at Buffalo Mountain Windfarm, eastern

Tennessee. Master's Thesis, University of Tennessee, 2004.

http://trace.tennessee.edu/utk_gradthes/2137.

Ford, W. M., M. A. Menzel, J. M. Menzel, and D. J. Welch. 2002. Influence of summer

temperature on sex ratios in eastern red bats (Lasiurus borealis). The American Midland





Frick, W. F., E. F. Baerwald, J. F. Pollock, R. M. R. Barclay, J. A. Szymanski, T. J. Weller, A. L.

Russell, S. C. Loeb, R. A. Medellin, and L. P. McGuire. 2017. Fatalities at wind turbines

may threaten population viability of a migratory bat. Biological Conservation 209:172–

177.

Furmankiewicz, J., and M. Kucharska. 2009. Migration of bats along a large river valley in

southwestern Poland. Journal of Mammalogy 90:1310–1317.

Griffin, D. R. 1971. The importance of atmospheric attenuation for the echolocation of bats

(Chiroptera). Animal Behaviour 19:55–61.

Grodsky, S., C. S. Jennelle, D. Drake, and T. Virzi. 2012. Bat mortality at a wind-energy facility

in southeastern Wisconsin. Wildlife Society Bulletin 36.

Hamilton, R. 2012. Spatial and temporal activity of migratory bats at landscape features.

Electronic Thesis and Dissertation Repository. https://ir.lib.uwo.ca/etd/886.

Hayes, M. A. 2013. Bats killed in large numbers at United States wind energy facilities.

BioScience 63:975–979.

Holland, R. A., and M. Wikelski. 2009. Studying the migratory behavior of individual bats:

current techniques and future directions. Journal of Mammalogy 90:1324–1329.

Johnson, G. D., W. P. Erickson, M. Dale Strickland, M. F. Shepherd, D. A. Shepherd, and S. A.

Sarappo. 2003. Mortality of bats at a large-scale wind power development at Buffalo

Ridge, Minnesota. The American Midland Naturalist 150:332–342.

103

Jonasson, K. A., and C. G. Guglielmo. 2016. Sex differences in spring migration timing and

body composition of silver-haired bats Lasionycteris noctivagans. Journal of

Mammalogy 97:1535–1542.

Kerns, J., and J. Horn. 2005. Relationships between bats and wind turbines in Pennsylvania and

West Virginia: An assessment of fatality search protocols, patterns of fatality, and

behavioral interactions with wind turbines.

http://www.batsandwind.org/pdf/postconpatbatfatal.pdf. Accessed 20 Jan 2016.

Kniowski, A. B. 2016. Landscape influences on spatial patterns of white-tailed deer herbivory

and condition indices in the central Appalachian Mountains. Virginia Polytechnic

Institute and State University.

Kniowski, A. B., and W. M. Ford. 2017. Predicting intensity of white-tailed deer herbivory in the

central Appalachian Mountains. Journal of Forestry Research.

https://doi.org/10.1007/s11676-017-0476-6.

Kunz, T. H. 1973. Resource utilization: Temporal and spatial components of bat activity in

central Iowa. Journal of Mammalogy 54:14–32.

Kunz, T. H. 2013. Ecology of bats. Springer Science & Business Media.

Kunz, T. H., E. B. Arnett, W. P. Erickson, A. R. Hoar, G. D. Johnson, R. P. Larkin, M. D.

Strickland, R. W. Thresher, and M. D. Tuttle. 2007. Ecological impacts of wind energy

development on bats: questions, research needs, and hypotheses. Frontiers in Ecology and

the Environment 5:315–324.

Kunz, T. H., and M. B. Fenton. 2006. Bat ecology. University of Chicago Press.

Kunz, T. H., and L. F. Lumsden. 2003. Ecology of cavity and foliage roosting bats. Pages 2–90

in T. H. Kunz and M. B. Fenton, editors. Bat Ecology. University of Chicago Press,

Chicago, Illinois.

Kurta, A. 1995. Mammals of the Great Lakes Region. Revised edition. The University of

Michigan Press, Ann Arbor, Michigan, USA.

La Sorte, F. A., D. Fink, W. M. Hochachka, A. Farnsworth, A. D. Rodewald, K. V. Rosenberg,

B. L. Sullivan, D. W. Winkler, C. Wood, and S. Kelling. 2014. The role of atmospheric

conditions in the seasonal dynamics of North American migration flyways. Journal of

Biogeography 41:1685–1696.

Lacki, M. J., J. P. Hayes, and A. Kurta. 2007. Bats in forests: conservation and management.

JHU Press.

Lemen, C., P. W. Freeman, J. A. White, and B. R. Andersen. 2015. The problem of low

agreement among automated identification programs for acoustical surveys of bats.

Western North American Naturalist 75:218–225.

104

Limpens, H., and K. Kapteyn. 1991. Bats, their behaviour and linear landscape elements. Myotis

29:39–48.

Lindberg, S. E., D. Silsbee, D. A. Schaefer, J. G. Owens, and W. Petty. 1988. A comparison of

atmospheric exposure conditions at high- and low- elevation forests in the southern

Appalachian Mountain Range. Pages 321–344 in. Acid Deposition at High Elevation

Sites. NATO ASI Series, Springer, Dordrecht.

Lott, K. D. 2008. Daily and seasonal patterns of bat activity along central appalachian ridges: a

thesis in applied ecology and conservation biology. Frostburg State University.

Martin, C. M., E. B. Arnett, R. D. Stevens, and M. C. Wallace. 2017. Reducing bat fatalities at

wind facilities while improving the economic efficiency of operational mitigation.

Journal of Mammalogy 98:378–385.

McGuire, L. P., C. G. Guglielmo, S. A. Mackenzie, and P. D. Taylor. 2012. Migratory stopover

in the long-distance migrant silver-haired bat, Lasionycteris noctivagans. Journal of

Animal Ecology 81:377–385.

McGuire, L. P., K. A. Jonasson, and C. G. Guglielmo. 2014. Bats on a budget: torpor-assisted

migration saves time and energy. PLoS ONE 9:e115724.

McIlmoil, R., and E. Hansen. 2010. The decline of Central Appalachian coal and the need for

economic diversification. Thinking Downstream: White Paper 1.

Møller, A. P. 2013. Long-term trends in wind speed, insect abundance and ecology of an

insectivorous bird. Ecosphere 4:1–11.

Navigant Consulting, Inc. 2017. Wind Energy: Jobs & Economic Benefits in all 50 States.

https://www.awea.org/gencontentv2.aspx?ItemNumber=9852. Accessed 2 Feb 2018.

Newton, I., K. Brockie, and ebrary, Inc. 2008. The migration ecology of birds. Elsevier-

Academic Press, Amsterdam.

Quine, C., and B. Gardiner. 2007. Understanding how the interaction of wind and trees results in

windthrow, stem breakage, and canopy gap formation. Plant Disturbance Ecology-the

Process and the Response 103–155.

R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. http://www.R-project.org/.

Ray, P. S., editor. 1986. Mesoscale meteorology and forecasting. American Meteorological

Society, Boston, MA.

Reynolds, D. S. 2006. Monitoring the potential impact of a wind development site on bats in the

Northeast. Journal of Wildlife Management 70:1219–1227.

105

Richardson, W. J. 1978. Timing and amount of bird migration in relation to weather: a review.

Oikos 30:224–272.

Riskin, D. K., J. Iriarte-Diaz, K. M. Middleton, K. S. Breuer, and S. M. Swartz. 2010. The effect

of body size on the wing movements of pteropodid bats, with insights into thrust and lift

production. Journal of Experimental Biology 213:4110–4122.

Salcedo, H., M. Fenton, M. Hickey, and R. Blake. 1995. Energetic consequences of flight speeds

of foraging red and hoary bats (Lasiurus borealis and Lasiurus cinereus; Chiroptera:

Vespertilionidae). Journal of Experimental Biology 198:2245–2251.

Schielzeth, H. 2010. Simple means to improve the interpretability of regression coefficients.

Methods in Ecology and Evolution 1:103–113.

Smith, A. D., and S. R. McWilliams. 2016. Bat activity during autumn relates to atmospheric

conditions: implications for coastal wind energy development. Journal of Mammalogy

97:1565–1577.

Soulé, P. T. 1998. Some spatial aspects of Southeastern United States climatology. Journal of

Geography 97:142–150.

Timm, R. M. 1989. Migration and molt patterns of red bats, Lasiurus borealis (Chiroptera:

Vespertilionidae), in Illinois. Bulletin of the Chicago Academy of Sciences 14(3):1–7.

Turbill, C., and F. Geiser. 2006. Thermal physiology of pregnant and lactating female and male

long-eared bats, Nyctophilus geoffroyi and N. gouldi. Journal of Comparative Physiology

B 176:165–172.

Tuttle, M. D. 2004. Wind energy & the threat to bats. Bat Conservation International.

http://www.batcon.org/resources/media-education/bats-magazine/bat_article/10.

Accessed 10 May 2016.

USFWS. 2017. Indiana bat summer survey guidance - automated acoustic bat id software

programs.

https://www.fws.gov/midwest/endangered/mammals/inba/surveys/inbaAcousticSoftware.

html. Accessed 26 Oct 2017.

Voigt, C. C., K. Schneeberger, S. L. Voigt-Heucke, and D. Lewanzik. 2011. Rain increases the

energy cost of bat flight. Biology Letters DOI: 10.1098/rsbl.2011.0313.

Wei, T., and V. Simko. 2016. corrplot: Visualization of a correlation matrix. https://cran.r-

project.org/web/packages/corrplot/index.html. Accessed 24 Aug 2017.

Weller, T. J., and J. A. Baldwin. 2012. Using echolocation monitoring to model bat occupancy

and inform mitigations at wind energy facilities. The Journal of Wildlife Management

76:619–631.

106

Wildlife Acoustics - Overview of Kaleidoscope Pro 3 Analysis Software.

https://www.wildlifeacoustics.com/products/kaleidoscope-software-ultrasonic. Accessed

26 Oct 2017.

Wolbert, S. J., A. S. Zellner, and H. P. Whidden. 2014. Bat activity, insect biomass, and

temperature along an elevational gradient. Northeastern Naturalist 21:72–85.

Wood, S. 2017. mgcv: Mixed GAM computation vehicle with automatic smoothness estimation.

https://cran.r-project.org/web/packages/mgcv/index.html. Accessed 28 Nov 2017.

107

Tables

Table 2-1: Variables used in candidate models representing hypotheses regarding migratory bat


during autumn 2015 and 2016, and spring 2016 and 2017. Variables were used in different

combinations, and highly correlated variables were not included within a single candidate model.

Variable name Variable Explanation

Date week of year for fall; day of year for spring

Hour hour of acoustic recording; 0-11

Avg. Temp. mean hourly temperature

Delta Temp. change in mean hourly temperature from 4 hours prior

Avg. Wind mean hourly wind speed

Avg. Wind Profit mean hourly wind profit; combination of wind speed and direction

Avg. Pressure mean hourly barometric pressure

Delta Pressure change in mean hourly pressure from 6 hours prior

Relative elevation proximal to valley floor, mid-slope, or ridgetop

108




during autumn 2015 and 2016, and spring 2016 and 2017.


Date

Bat activity varies

in intensity and spatially by

date

(Cryan 2003, Baerwald and Barclay

2009, Hamilton 2012)

Hour of night Bat activity varies throughout

each night

(Kunz 1973, Kunz and Lumsden

2003, Kunz 2013)

Mean hourly temperature Ambient temperatures affect

bat activity

(Reynolds 2006, Weller and

Baldwin 2012, Smith and

McWilliams 2016)

Change in mean

temperature from hours

prior

Bats likely can sense even


and adjust behavior

accordingly

(Kunz 2013, Smith and

McWilliams 2016)

Mean hourly wind speed



speeds




Mean hourly wind profit

Bats likely can sense wind

direction and speed and

adjust behavior accordingly

(Smith and McWilliams 2016)

Mean hourly barometric

pressure

Bats may respond to

barometric pressure due to

flight conditions or

associated conditions


Bender and Hartman 2015, Smith


Change in mean pressure

from hours prior

Bats may respond to

changing pressure and

associated changing

conditions

(Baerwald and Barclay 2011, Smith


Relative elevation

Bat activity varies along an

elevational gradient (Wolbert et al. 2014)

109




Appalachians, Virginia, during autumn 2015 and 2016.

Variable Estimate Std..Error

Lower

C.I.

Upper

C.I.

(Intercept) -1.51 0.04 -1.59 -1.42

Precipitation -0.35 0.15 -0.65 -0.05

Delta Temp. -0.24 0.05 -0.33 -0.15

Low elevation 0.22 0.09 0.05 0.39

Mid elevation -0.22 0.09 -0.38 -0.05

Pressure 0.21 0.04 0.14 0.29

Wind speed -0.19 0.04 -0.27 -0.11

Delta pressure 0.07 0.04 -0.01 0.14

Wind profit -0.04 0.04 -0.11 0.03

Smoothed Terms edf Ref.df

Temperature 7.56 7.56

Hour 2.77 2.77

Week 6.84 6.84

110



central Appalachians, Virginia, during autumn 2015 and 2016.

Variable Estimate Std..Error Lower C.I. Upper C.I.

(Intercept) -2.57 0.05 -2.67 -2.47

Delta Temp. -0.45 0.05 -0.56 -0.35

Mid elevation -0.20 0.10 -0.40 0.01

Pressure 0.14 0.04 0.06 0.23

Delta pressure 0.12 0.04 0.03 0.20

Wind speed -0.11 0.05 -0.21 -0.02

Low elevation 0.03 0.10 -0.17 0.23


Temperature 7.19 7.19

hour 1.56 1.56

Week 7.19 7.19

111

Table 2-5: Relationship between hourly activity of eastern red bats (Lasiurus borealis) and




(Intercept) -2.33 0.04 -2.42 -2.25

Precipitation -0.66 0.19 -1.03 -0.29

Wind speed -0.49 0.04 -0.58 -0.41

Delta pressure -0.10 0.04 -0.18 -0.01

Wind profit 0.06 0.04 -0.02 0.14


Hour 4.29 4.29

112




Appalachians, Virginia, during spring 2016 and 2017.


(Intercept) -0.93 0.05 -1.04 -0.83

Precipitation -1.18 0.23 -1.64 -0.72

Temp. 0.95 0.06 0.82 1.07

Wind speed -0.16 0.07 -0.29 -0.03

Delta pressure -0.12 0.06 -0.23 -0.01

Wind profit 0.07 0.07 -0.06 0.20


Hour 4.94 4.94

Day number 8.33 8.33

113



central Appalachians, Virginia, during spring 2016 and 2017.


(Intercept) -1.77 0.06 -1.89 -1.66

Precipitation -1.97 0.27 -2.49 -1.44

Temp. 1.02 0.07 0.87 1.16

Wind speed -0.26 0.08 -0.41 -0.11

Delta pressure -0.13 0.07 -0.26 0.01

Wind profit 0.05 0.08 -0.10 0.20


Hour 4.05 4.05


114

Table 2-8: Relationship between hourly activity of eastern red bat (Lasiurus borealis) and




(Intercept) -2.76 0.06 -2.87 -2.64

Precipitation -0.95 0.26 -1.46 -0.45

Wind speed -0.72 0.07 -0.87 -0.58

Temp. 0.66 0.07 0.52 0.79

Delta pressure -0.12 0.07 -0.25 0.01

Wind profit 0.07 0.07 -0.08 0.21


Hour 6.11 6.11


115

Table 2-9: Relationship between hourly activity of hoary bats (Lasiurus cinereus) and regional

hourly atmospheric conditions along 5 ridgelines and adjacent sideslopes in the central


Variable Estimate Std.Error Lower C.I. Upper C.I.

(Intercept) -2.23 0.06 -2.35 -2.12

Temp. 0.91 0.06 0.79 1.03

Precipitation -0.41 0.23 -0.87 0.05

Temp.*Day number 0.17 0.06 0.06 0.29


Hour 1.00002 1.00002

Day number 5.02825 5.02825

116

Figures

Figure 2-1: Approximate locations of five ridges sampled in central Appalachians of Virginia.

The ridges sampled in Giles and Bath counties occur in the Ridge and Valley sub-physiographic

province, whereas the ridges sampled in Rockingham and Madison counties occur in the Blue

Ridge sub-physiographic province. Acoustic sampling occurred during autumn 2015 and 2016

and spring 2016 and 2017.

117

Figure 2-2: Example of acoustic sampling survey sites on ridgelines and adjacent sideslopes.

Acoustic detectors were deployed in this manner along five ridgelines in the central

Appalachians, Virginia, during autumn 2015 and 2016 and spring 2016 and 2017.

118

Figure 2-3: Partial effects plot of the relationship between date and migratory bat species (silver-haired bat-Lasionycteris noctivagans,

eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95% confidence intervals shade


119

Figure 2-4: Partial effects plot of the relationship between mean hourly temperature (Celsius) and migratory bat species (silver-haired

bat-Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95%

confidence intervals) along five ridgelines in the central Appalachians, Virginia, during autumn 2015 and 2016.

120

Figure 2-5: Partial effects plot of the relationship between hour of night, relative elevation, and migratory bat species (silver-haired

bat-Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95%

confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during autumn 2015 and 2016.

121

Figure 2-6: Partial effects plot of the relationship between hourly barometric pressure and migratory bat species (silver-haired bat-

Lasiurus noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95%

confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during autumn 2015 and 2016.

122

Figure 2-7: Partial effects plot of the relationship between change in ambient temperature from 4 hours prior and migratory bat species

(silver-haired bat-Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per

hour (with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during autumn 2015 and

2016. Activity was greater when the drop in temperature from 4 hours prior was greater.

123

Figure 2-8: Partial effects plot of the relationship between date and silver-haired bat (Lasionycteris noctivagans) echolocation passes

per hour (with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during autumn 2015

and 2016.

124

Figure 2-9: Partial effects plot of the relationship between hour of sampling each night and silver-haired bat (Lasionycteris

noctivagans) echolocation passes per hour (with 95% confidence intervals shade gray) along five ridgelines in the central


125

Figure 2-10: Partial effects plot of the relationship between mean hourly ambient temperature (Celsius) and silver-haired bat

(Lasionycteris noctivagans) echolocation passes per hour (with 95% confidence intervals shade gray) along five ridgelines in the

central Appalachians, Virginia, during autumn 2015 and 2016.

126

Figure 2-11: Partial effects plot of the relationship between change in ambient temperature from 4 hours prior and silver-haired bats

(Lasionycteris noctivagans) echolocation passes per hour (with 95% confidence intervals shade gray) along five ridgelines in the

central Appalachians, Virginia, during autumn 2015 and 2016. Activity was greater when the drop in temperature from 4 hours prior

was greater.

127

Figure 2-12: Partial effects plot of relationship between hour of sampling each night and eastern red bat (Lasiurus borealis)

echolocation passes per hour (with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia,

during autumn 2015 and 2016.

128

Figure 2-13: Partial effects plot of the relationship between mean hourly wind speed (KPH) and eastern red bat (Lasiurus borealis)


during autumn 2015 and 2016.

129

Figure 2-14: Partial effects plot of the relationship between date and eastern migratory bat species (silver-haired bat-Lasionycteris

noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95% confidence

intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017.

130

Figure 2-15: Partial effects plot of the relationship between sampling hour of night and migratory bat species (silver-haired bat-

Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour (with 95%

confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017.

131

Figure 2-16: Partial effects plot of the relationship between ambient hourly temperature (Celsius) and migratory bat species (silver-

haired bat-Lasionycteris noctivagans, eastern red bat-Lasiurus borealis, hoary bat-Lasiurus cinereus) echolocation passes per hour

(with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017.

132

Figure 2-17: Partial effects plot of the relationship between date and silver-haired bat (Lasionycteris noctivagans) echolocation passes

per hour (with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during spring 2016

and 2017.

133

Figure 2-18: Partial effects plot of the relationship between sampling hour of night and silver-haired bat (Lasionycteris noctivagans)


during spring 2016 and 2017.

134

Figure 2-19: Partial effects plot of the relationship between hourly ambient temperature (Celsius) and silver-haired bat (Lasionycteris



135

Figure 2-20: Partial effects plot of the relationship between hourly wind speed (KPH) and silver-haired bat (Lasionycteris



136

Figure 2-21: Partial effects plot of the relationship between date and eastern red bat (Lasiurus borealis) echolocation passes per hour

(with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017.

137

Figure 2-22: Partial effects plot of the relationship between sampling hour of night and eastern red bat (Lasiurus borealis)



138

Figure 2-23: Partial effects plot of the relationship between hourly ambient temperature (Celsius) and eastern red bat (Lasiurus

borealis) echolocation passes per hour (with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians,

Virginia, during spring 2016 and 2017.

139

Figure 2-24: Partial effects plot of the relationship between hourly wind speed (KPH) and eastern red bat (Lasiurus borealis)



140

Figure 2-25: Partial effects plot of the relationship between date and hoary bat (Lasiurus cinereus) echolocation passes per hour (with

95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during spring 2016 and 2017.

141

Figure 2-26: Partial effects plot of the relationship between sampling hour of night and hoary bat (Lasiurus cinereus) echolocation

passes per hour (with 95% confidence intervals shade gray) along five ridgelines in the central Appalachians, Virginia, during spring

2016 and 2017.

142

Figure 2-27: Partial effects plot of the relationship between hourly ambient temperature (Celsius) and hoary bat (Lasiurus cinereus)



143

Figure 2-28: Photograph of an eastern red bat (Lasiurus borealis) roosting in litter (left), and approximate location of roost on hillside

(right) at Pandapas Pond Recreational area, George Washington and Jefferson National Forest, Montgomery County, VA, on

03/08/2015. Photograph credit: Andrew Kniowski.

144

Chapter 3: Impacts of White-nose Syndrome on maternity colony day roost selection by Myotis

septentrionalis in the central Appalachians

Abstract

Most extant northern long-eared bat maternity colony day-roost data were gathered prior

to the onset of White-nose Syndrome (WNS). Despite the informational need with threatened

status under the Endangered Species Act, the difficulty in catching northern long-eared bats has

meant that locating and describing post-WNS day-roosts to compare to pre-WNS is limited. Five

years after the advent of WNS in Virginia, I captured and radio-tracked pregnant or lactating

female northern long-eared bats to day-roosts in in the central Appalachians of Bath County,

Virginia. I compared recorded day-roost characteristics to those recorded pre-WNS at the nearby

Fernow Experimental Forest, Tucker County, West Virginia within similar vegetation types and

elevations. Post-WNS, I found that day-roost trees/snags exhibited smaller DBH values, total

heights, and roost heights. Because I found no differences pre- verses post-WNS in canopy

closure, surrounding forest basal area, and day-roost condition, I suggest northern long-eared

bats post-WNS were still selecting similar day-roost types and structural conditions relative to

the inherent differences that existed between the Virginia and West Virginia forests. Conversely,

documented changes in some day roost characteristics (e.g., roost tree size) may not be a

behavioral change; instead, these trends may be a result of reduced population sizes and smaller

maternity colony sizes that did not require larger day-roosts. Moving forward, I suggest that pre-

WNS day-roost data continue to be incorporated into current management plans, with the caveat

that post-WNS fine-scale roost metrics, when available, be given greater weight.

145

Introduction

Prior to the onset of White-nose Syndrome (WNS), day-roosting ecology of the northern

long-eared bat (Myotis septentrionalis, MYSE) had been reasonably well-documented (Owen et

al. 2002, Perry and Thill 2007, Johnson et al. 2009, 2012, Rojas et al. 2017, Silvis et al. 2012,

2015a). During the summer maternity season, MYSE primarily roosted in tree/snag cavities or,

to a lesser extent, exfoliating bark or human-made structures (Sasse and Pekins 1996, Caceres

and Barclay 2000, Lacki and Schwierjohann 2001, Ford et al. 2006, Johnson et al. 2009, Lacki et

al. 2009, Silvis et al. 2012, 2015a, Stein and White 2016).

Pregnant and lactating female MYSE roost separately from male conspecifics during the

summer months, forming maternity colonies (Caceres and Barclay 2000, Ford et al. 2006).

Northern long-eared bat maternity colonies are defined as the assemblage of females that use the

same day-roosts within a single summer season (Silvis et al. 2014, Ford et al. 2016a). Historic

(prior to WNS) emergence count data in the central Appalachians indicated that colonies could

be quite large, > 70 individuals (Owen et al. 2002, Rojas et al. 2017), but generally were smaller

groups. For example, immediately prior to WNS, Johnson et al. (2011) observed a mean of 27.2±

9.1 individuals per colony in West Virginia. Larger maternity colony size may be associated with

improved social structure and colony dynamics (Ford et al. 2016a), but the effects of colony size

on roost selection and productivity of maternity colonies largely remains unknown. Also, prior to

WNS, MYSE maternity colonies characteristically were comprised of closely-related

individuals, and displayed inter-annual site fidelity (Perry 2011, Patriquin et al. 2013, Silvis et al.

2015b). These biological traits may influence colony sizes/dynamics that have subsequently been

impacted by overall population declines (Francl et al. 2012, Reynolds et al. 2016, USFWS 2017).

146

A single maternity colony normally uses multiple roost sites within a definable forest

patch, frequently switching roosts in a non-random assorting intra-colony network dynamic

(Caceres and Barclay 2000, Johnson et al. 2009, Silvis et al. 2014, Patriquin et al. 2016, Silvis et

al. 2016a). A wide variety of tree species are used, including oaks (Quercus spp.), hickories

(Carya spp.), black locust (Robinia pseudoacacia), sassafras (Sassafras albidum), and maples

(Acer spp.). Throughout the central Appalachians and to the immediate west in the Ohio River

Valley, bats strongly selected black locust and sassafras in previous studies (Lacki and

Schwierjohann 2001, Ford et al. 2006, Johnson et al. 2009, Lacki et al. 2009, Silvis et al. 2012,

2014). These tree species provide longer-lasting roost sites as either live trees or snags compared

to other species with faster decay rates (Sasse and Pekins 1996, Lacki and Schwierjohann 2001,

Ford et al. 2006, Johnson et al. 2009, Lacki et al. 2009, Silvis et al. 2014). Indeed, most

documented day-roosts trees used by MYSE in the central Appalachians are declining live trees

or snags with cavities (Owen et al. 2002, Ford et al. 2006, Johnson et al. 2009, Ford et al.

2016a). Tree selection is related to the characteristics of individual roost trees and the

surrounding forest stand (Cryan et al. 2001, Menzel et al. 2002b, Carter and Feldhamer 2005,

Johnson et al. 2009).

Prior to WNS, research from the northern part of MYSE range in Canada and New

England or at the higher elevations encountered in the central Appalachians has shown that

maternity colonies select summer day-roost sites for thermoregulatory benefits (warmth) during

fetal development, lactation, and juvenile development (Garroway and Broders 2008, Henderson

and Broders 2008, Patriquin et al. 2016). Less canopy closure and slope aspects that increased

solar exposure and radiation were generally regarded as important characteristics of day-roosts

(Desta et al. 2004, Boyles 2007, Johnson et al. 2009, Silvis 2014). Conversely, where

147

temperature minimums and solar exposure were not considered a limiting factor, day-roost

selection may not follow these patterns (Silvis et al. 2012, Patriquin et al. 2016). A commonality

from pre-WNS day-roost research, at least in the central Appalachians and Ohio River Valley,

has been that in most forest types and management regimes, day-roost availability has not been a

restrictive component of MYSE ecology (Menzel et al. 2002b, Owen et al. 2002, Ford et al.

2006, Lacki et al. 2009, Silvis et al. 2012, Ford et al. 2016a).

Populations of MYSE have declined precipitously due to WNS (Frick et al. 2016,

Reynolds et al. 2016), creating an imperative for wildlife managers to recognize how MYSE

maternity roosting ecology may be changing through this mortality event. Population declines

have been so severe (> 90% mortality), the species was determined to be federally-threatened by

the U.S. Fish and Wildlife Service (Department of the Interior 2015). Due to the current scarcity

of MYSE across the landscape, post-WNS study of maternity colony roosting ecology is

extremely challenging, leaving the effects of WNS on MYSE maternity colony roosting ecology

largely unknown. If roosting ecology of MYSE maternity colonies changes concurrently with

population numbers, using pre-WNS findings to guide management of roost resources may fail

to avoid activities that negatively affect this threatened species (Langwig et al. 2012), or provide

inadequate direction for forest management to provide day-roosting habitat to facilitate recovery

(Silvis et al. 2016b). It is possible that MYSE will become a primary regulatory driver of upland

forest management in much of the eastern United States if up-listed under the Endangered

Species Act as declines continue, as they are forest habitat generalists in terms of their day-

roosting and foraging habitat use (Ford et al. 2016b). Accordingly, managers need updated day-

roost information that reflects post-WNS roosting ecology.

148

My priority objective was to determine if differences existed between pre- and post-WNS

MYSE maternity colony roost selection ecology in the central Appalachians. Due to severe

regional WNS-related population declines, I clearly expected that located maternity colonies

would be comprised of fewer individuals than those pre-WNS colonies in the central

Appalachians (Johnson et al. 2009, Silvis et al. 2012). Regardless of colony size, I expected day-

roost ecology generally would be similar to pre-WNS findings, and roost selection differences

would be minimal.

Methods

Study Area

My study was conducted on the George Washington and Jefferson National Forest and to

a lesser extent, on adjacent private lands, in Bath County, Virginia. The forests of this area are

relatively xeric-to moderately mesic oak (Quercus spp.) and montane pine (Pinus spp.)

associations dominated by chestnut oak (Quercus montana), white oak (Quercus alba), Virginia

pine (Pinus virginiana), and pitch pine (Pinus rigida; Braun 1950). In lower elevations and along

riparian corridors, more mesic species such as white pine (Pinus strobus), eastern hemlock

(Tsuga canadensis), and red maple (Acer rubrum) predominate. It is widely accepted that pre-

European settlement, this region was comprised of more xeric oak-pine savannas with mesic

hemlock-white pine mixed forest restricted to riparian areas (Nowacki and Abrams 2008, Arthur

et al. 2012). Fire suppression policies starting in the early 1900s drastically changed forest

composition and structure, increasing the abundance of fire-intolerant species such as red maple,

American beech (Fagus grandifolia), blackgum (Nyssa sylvatica), and tulip poplar (Liriodendron

tulipifera). Burning resumed as a restoration tool in the past few decades (Abrams 1992, Brose et

149

al. 2001, Arthur et al. 2012). White-nose Syndrome was confirmed to have reached hibernacula

in Bath County, Virginia, in February 2009 (Reynolds et al. 2016).

For comparison to post-WNS MYSE maternity colony day-roosts, I used pre-WNS

MYSE maternity colony day-roost data, 2007-2008, from the Fernow Experimental Forest (FEF)

in Tucker County, West Virginia (Johnson et al. 2009). This site was on the Allegheny Plateau

100 km to the northwest of Bath County in a largely similar forest system and management

regime, though slightly more mesic with a larger proportion of mesic, cove hardwood

associations (Johnson et al. 2009). For a more detailed description, see Johnson et al. (2009).

Data Collection

I captured MYSE using mist nets (38mm mesh, reduced bag, Avinet, Portland, ME) set

up over small first-order stream corridors, ephemeral pools, and single-lane forest roads from

late May to late July in 2015 and 2016. I recorded age (through epiphyseal-diaphyseal fusion

estimation; Anthony 1988), mass, right forearm length, sex, and reproductive condition for each

bat captured (Menzel et al. 2002a). I affixed radio transmitters (LB-2, 0.47 g, Holohil Systems

Ltd. Woodlawn, ON, Canada) to captured pregnant or lactating female bats, between the

scapulae, using Perma-Type surgical cement (Perma-Type Company Inc., Plainville, CT). I put

uniquely-numbered wing bands (2.4mm, Porzana Limited, Icklesham, East Sussex, U.K.) on the

forearms of captured bats to identify recaptured individuals (Silvis et al. 2012). I used radio

telemetry to track bats during daylight hours to locate day-roosts (Wildlife Materials TRX-1000

receivers and 3-element yagi antennae, Murphysboro, IL; Johnson et al. 2009). To capture

additional members of putative maternity colonies, I surrounded located day-roosts with mist

nets following the methods of Silvis et al. (2014). I expected to detect the majority of individuals

within a maternity colony through tracking and capture or day-roost exit counts (Silvis et al.

150

2015b). Additionally, I performed nightly exit counts on day-roost trees that were not conducive

to mist net capture. I attached radio-transmitters to all female bats captured in the netted colony,

and tracked bats daily to locate additional day-roosts. In the event of a bat losing a transmitter

prior to recapture, I reattached a new transmitter. I tracked bats to day-roosts until the transmitter

batteries expired or until they fell off the bats.

Following the methods of Johnson et al. (2009), I measured the following tree

characteristics of each day-roost tree found: locations (Garmin Rino GPS units, Olathe, KS), tree

species, diameter at breast height (dbh tape, Forestry Suppliers Inc., Jackson, MS), decay stage,

crown class, tree height (using a clinometer, Brunton, Louisville, CO), roost type where

observable, and roost height where observable. I visually estimated the percentage of bark on

each roost tree. I categorized decay stage of each roost tree using numbers corresponding to a

qualitative value: 1 = live, 2 = declining, 3 = recent dead, 4 = loose bark, 5 = no bark, 6 = broken

top, 7 = broken bole (Cline et al. 1980). I categorized crown class using a similar scheme: 1 =

suppressed, 2 = intermediate, 3 = codominant, 4 = dominant (Nyland 2002). I recorded similar

characteristics for the four trees nearest the roost tree using the point-centered quarter method

and measured their distances to each roost tree (Mitchell 2010). I used a concave densitometer

(Forestry Suppliers Inc., Jackson, MS) to record canopy closure and a 20-factor prism (JIM-

GEM®, Jackson, MS) to measure the basal area around each roost tree.

I used ArcMap 10.3.1® software (ESRI, Redlands, CA) to generate 2 random points for

each roost tree found in the summer of 2015 and 2016. I placed half of these random points

within a minimum convex polygon of the roost trees from each maternity colony, and the

remaining random points were within a buffer around the minimum convex polygon. I located

151

these random points in the field using GPS, and chose the closest tree for random tree

measurement. I measured the same variables at random trees as day-roosts.


To measure differences in tree metrics between pre-WNS and post-WNS roost trees and

between post-WNS roost trees and random trees, I used two-sample Wilcoxon tests for simple

comparisons of continuous measures, similar to the protocol of Silvis et al. (2012). Due to small

sample sizes, I pooled roost tree data from summer 2015 and 2016. I used Fisher’s Exact Tests

(FET) to determine equitability of the distribution of categorical variables between pre and post-

WNS roost trees and between post-WNS roost trees and random trees. For all statistical analyses,

I used the R statistical program, version 3.2.3 (R. Development Core Team 2014). I established

significance for all tests at α ≤ 0.05.

Results

Between May 20 and July 31 in 2015, and between May 23 and July 29 in 2016, I

captured and affixed transmitters to three female MYSE (three pregnant but zero lactating upon

initial capture) in 2015 and seven (one pregnant and six lactating upon initial capture) in 2016,

respectively. Day-roost exit counts at single roost trees ranged from zero (visited tree known to

have bats on prior occasions) to five bats in 2015 (mean = 1.73, SD = 2.1), and from zero to

seven in 2016 (mean = 1.43, SD = 2.57). I considered maternity colonies from each year to be

distinct colonies, although each group of day-roosts was in close proximity on the landscape

(Figure 3-1). I did not capture juvenile bats in either year, and each colony from 2015 and 2016

appeared to disaggregate by June 12 and June 10, respectively. I continued netting and tracking

efforts within the initial roosting areas in 2015 and 2016, but was unsuccessful in catching

additional MYSE or recapturing previously-caught MYSE.

152

I located six day-roosts in 2015 and nine day-roosts in 2016, representing three and six

tree species, respectively, for a total of six tree species observed (Table 3-1). The tree species

used as day-roosts post-WNS were significantly different (number of species and proportion of

use) than the species used pre-WNS (Table 3-2, P = 0.006, FET). Additionally, fewer tree

species were used as day-roosts post-WNS compared to tree species available on the landscape

(Table 3-2, P = 0.009, FET).

Of the 15 day-roosts that I observed over the duration of the study, 11 of 15 were snags, a

similar proportion as those located pre-WNS (Table 3-4, P = 0.49, FET). Furthermore, day-

roosts located post-WNS were in snags more than expected based on availability in the

surrounding forest stands (Table 3-3, P = 0.025, FET). Thirteen day-roosts found post-WNS

were cavity roosts whereas two were exfoliating bark roosts, analogous to day-roosts found pre-

WNS (Table 3-4, P = 1, FET). However, day-roost trees, located post-WNS, were significantly

shorter than day-roost trees located pre-WNS (Table 3-4, W = 757, P = 0.005), but similar to

randomly selected trees (Table 3-3, W = 262, P = 0.38). Roost heights in day-roosts found post-

WNS were significantly lower than roost heights in day-roosts found pre-WNS (Table 3-4, W =

592, P = 0.008). Average dbh of post-WNS day-roosts was significantly less than that of pre-

WNS day-roosts (Table 3-4, W = 795, P = 0.001), yet similar to randomly selected trees (Table

3-3, W = 215.5, P = 0.83). Percentage of bark remaining on the bole was similar between day-

roost found post-WNS and those found pre-WNS (Table 3-4, W = 416, P = 0.24), but day-roosts

located post-WNS had significantly less bark remaining compared to randomly-selected trees

(Table 3-3, W = 329.5, P = 0.006).

The forest stand basal area surrounding day-roosts located post-WNS was similar to

stands surrounding day-roosts located pre-WNS and similar to randomly selected trees (Table 3-

153

3 and Table 3-4, W = 550, P = 0.71, W = 193.5, P = 0.45, respectively). Day-roosts located post-

WNS had significantly greater percent canopy closure than day-roosts located pre-WNS (Table

3-2, W = 322, P = 0.02), but canopy closure was similar to randomly selected trees (Table 3-3,

W = 278, P = 0.21). Finally, 46.7% of day roosts located post-WNS were located on slopes with

southern aspects, significantly fewer than the 81.2% of day roosts located pre-WNS (Table 3-4,

P = 0.009, FET), but a similar percentage compared to 50.0% of randomly selected trees (Table

3-3, P = 1, FET).

Discussion

Results suggest that many of the characteristics of MYSE maternity colony day-roosts

remain unchanged, whereas others differed either post-WNS or due to the variation in forest

conditions between Bath County (Virginia) and the Fernow Experimental Forest (Tucker

County, West Virginia). The proportion of snags and cavity-roosts used, and amount of bark

remaining on day-roosts used was similar between maternity colonies located pre- and post-

WNS. Research prior to WNS in the central Appalachians near this study site showed that cavity

roosts are common amongst MYSE maternity colonies, offering protection, thermal benefits, and

are readily available in upland deciduous forests (Menzel et al. 2002b, Owen et al. 2002, Johnson

et al. 2009, Silvis 2014, Silvis et al. 2015a). Furthermore, snags are more likely to have cavities

than live trees, and the amount of bark remaining on bole is correlated with mortality condition

(Goodburn and Lorimer 1998, Fan et al. 2003, Eskelson et al. 2009). Results indicate that WNS

likely has not changed this integral part of MYSE maternity colony day-roosting ecology.

Perhaps most surprising were the significantly smaller diameters of day-roosts used by

post-WNS MYSE maternity colonies, compared to day-roosts used in pre-WNS surveys

(Johnson et al. 2009). Because the MYSE maternity colonies that I located were comprised of

154

only a few individuals, perhaps smaller diameter day-roosts offered greater thermoregulatory and

social benefits, as cavity volume is likely to be lower and fewer bats are required to fill overall

roost space. This could potentially reduce energetic costs associated with maintaining high body

temperatures (Hamilton and Barclay 1994, Willis et al. 2006, Garroway and Broders 2008,

Henderson and Broders 2008). Still, the effects of internal cavity size and characteristics on roost

selection ecology and reproductive behaviors of MYSE maternity colonies in a post-WNS

environment merits further study (Silvis et al. 2015c).

If small maternity colonies select day-roosts for increased/shared warmth due to fuller

cavities, I would also expect roosts to be located in places with a higher degree of solar radiation

for even greater roost warmth (Kerth et al. 2001, Lourenço and Palmeirim 2004, Boyles 2007,

Silvis et al. 2015a). However, I found no differences in canopy closure between day-roosts used

by maternity colonies post-WNS and randomly-selected trees. These data suggest that bats may

have selected for day-roosts based on criteria other than canopy closure, and/or there was less

availability of potential roost trees with smaller diameters and greater canopy openness on the

landscape. Similarly, I expected the majority of roost locations to be on warmer, southerly

aspects (Desta et al. 2004). However, available aspects are partly controlled to the orientation of

the overall linear ridges locally. Thus, day-roosts selected post-WNS appear to have been

located on the warmest areas at my study sites that did not lack availability of other roost

characteristics. Canopy closure and other microclimate characteristics such as slope and aspect

may not be as important as tree dbh/cavity size for maternity colony roost selection, especially

for smaller colonies. Indeed, social thermoregulation is more important than microclimate

characteristics at roost sites for big brown bat (Eptesicus fuscus) energy balance, and this may be

the case for MYSE maternity colonies as well (Willis and Brigham 2007).

155

If pregnancies are not viable or pups die before volancy, physiological constraints for

MYSE females may be relaxed. Non-reproductive females may be able to enter torpor regularly,

and thus may select day-roosts with characteristics other than warmth (unlikely behavior in

pregnant bats; Hamilton and Barclay 1994; Willis et al. 2006). Pettit and O’Keefe (2017)

observed big brown bats that lost pups due to effects of WNS, and this may be occurring in

MYSE maternity colonies as well. Though each female captured in 2015 and 2016 showed some

evidence of reproductive characteristics (i.e. pregnant and lactating), no juvenile bats were

captured and all individuals appeared to leave the landscape prior to when juveniles would have

become volant (Johnson et al. 2013). It is possible that both maternity colonies I initially located

in 2015 and 2016 moved as a social group to an entirely new geographic area where I could not

detect them. However, such colony-wide movement pre-volancy is unlikely (Silvis et al. 2014).

It also is possible that maternity colonies in 2015 and 2016 failed to successfully produce

offspring, corroborating prior research assessing population-scale impacts of WNS on MYSE

(Francl et al. 2012, Reynolds et al. 2016, Pettit and O’Keefe 2017). Nonetheless, my study shows

it is unlikely a major shift in day-roosting selection is occurring and thus recruitment failure

likely is the cause of changes in some other aspect of MYSE ecology.

Variation observed in day-roost selection between pre- and post-WNS MYSE maternity

colonies included in my study may be a product of different colony locations and the associated

surrounding forests. Although the forests surrounding the pre and post-WNS colonies used in

these analyses appear qualitatively similar (Braun 1950, Ford et al. 2005, Johnson et al. 2009),

there may be important differences in local forest management activities, topography, geology,

climate, and biotic communities which could relate to distinctly different day-roost ecology

between MYSE maternity colonies. Indeed, even local variability of maternity colony roost

156

selection has been reported, between years and within a single season across the MYSE

distribution (Garroway and Broders 2007, 2008, Silvis et al. 2012, 2015a). Regardless, the

forests are climatically and compositionally more similar than dissimilar, and offer a reasonable

starting point to determine how day-roost ecology of MYSE maternity colonies may be changing

with the protraction of WNS. I assumed maternity colonies that I located in Bath County,

Virginia, would have been comprised of more individuals prior to WNS-related declines, yet no

data exist on maternity colony size prior to WNS on the local landscape.

Despite the extreme population declines of MYSE in the past decade, understanding how

WNS impacts day-roosting ecology of MYSE maternity colonies is needed by managers trying

to protect remaining populations. As my study shows, MYSE maternity colonies post-WNS may

be selecting smaller trees than those selected by maternity colonies pre-WNS. However, because

of my small sample size and potential habitat variation between maternity colonies located in

West Virginia pre-WNS and those located in Virginia post-WNS, my comparative analyses

should be interpreted with caution. At a minimum, WNS appears to affect MYSE maternity

colony ecology by disrupting the reproductive cycle and reducing recruitment (Francl et al. 2012,

Reynolds et al. 2016). Research on day-roost ecology of MYSE maternity colonies where WNS

remains absent should continue, and researchers must be prepared to take advantage of future

opportunities to study MYSE maternity colonies where WNS is already present. These data

potentially will offer detailed insight into how WNS affects day-roosting ecology of MYSE

maternity colonies, thus helping managers implement best management practices to conserve

these threatened populations.

157

Literature Cited

Abrams, M. D. 1992. Fire and the Development of Oak Forests. BioScience 42:346–353.

Anthony, E. L. P. 1988. Age determination in bats. Pages 47–58 in T. H. Kunz, editor.

Ecological and behavioral methods for the study of bats. Smithsonian Institution Press,

Washington DC.

Arthur, M. A., H. D. Alexander, D. C. Dey, C. J. Schweitzer, and D. L. Loftis. 2012. Refining

the oak-fire hypothesis for management of oak-dominated forests of the eastern United

States. Journal of Forestry 110:257–266.

Boyles, J. G. 2007. Describing roosts used by forest bats: the importance of microclimate. Acta

Chiropterologica 9:297–303.



Brose, P., T. Schuler, D. van Lear, and J. Berst. 2001. Bringing fire back: the changing regimes

of the Appalachian mixed-oak forests. Journal of Forestry 99:30–35.

Caceres, M. C., and R. M. R. Barclay. 2000. Myotis septentrionalis. Mammalian Species 1–4.

Carter, T. C., and G. A. Feldhamer. 2005. Roost tree use by maternity colonies of Indiana bats

and northern long-eared bats in southern Illinois. Forest Ecology and Management

219:259–268.

Cline, S. P., A. B. Berg, and H. M. Wight. 1980. Snag characteristics and dynamics in Douglas-

fir forests, western Oregon. The Journal of Wildlife Management 44:773–786.

Cryan, P. M., M. A. Bogan, and G. M. Yanega. 2001. Roosting habits of four bat species in the

Black Hills of South Dakota. Acta Chiropterologica 3:48.

Department of the Interior: Fish and Wildlife Service. 2015. Listing the northern long-eared bat

with a rule under section 4(d) of the Act. Federal Register.

http://www.fws.gov/midwest/endangered/mammals/nlba/pdf/FRnlebProposed4dRule16Ja

n2015.pdf.

Desta, F., J. J. Colbert, J. S. Rentch, and K. W. Gottschalk. 2004. Aspect induced differences in

vegetation, soil, and microclimatic characteristics of an Appalachian watershed. Castanea

69:92–108.

Eskelson, B. N., H. Temesgen, and T. M. Barrett. 2009. Estimating cavity tree and snag

abundance using negative binomial regression models and nearest neighbor imputation

methods. Canadian Journal of Forest Research 39:1749–1765.

158

Fan, Z., D. R. Larsen, S. R. Shifley, and F. R. Thompson. 2003. Estimating cavity tree

abundance by stand age and basal area, Missouri, USA. Forest Ecology and Management

179:231–242.




Ford, W. M., F. O. Sheldon, J. W. Edwards, and J. L. Rodrigue. 2006. Robinia pseudoacacia

(Black Locust) as day-roosts of male Myotis septentrionalis (Northern Bats) on the

Fernow Experimental Forest, West Virginia. Northeastern Naturalist 13:15–24.

Ford, W. M., A. Silvis, J. B. Johnson, J. W. Edwards, and M. Karp. 2016a. Northern long-eared

bat day-roosting and prescribed fire in the central Appalachians, USA. Fire Ecology

12:13–27.

Ford, W. M., A. Silvis, J. L. Rodrigue, A. B. Kniowski, and J. B. Johnson. 2016b. Deriving

habitat models for northern long-eared bats from historical detection data: a case study

using the Fernow Experimental Forest. Journal of Fish and Wildlife Management 7:86–

98.

Francl, K. E., W. M. Ford, D. W. Sparks, and V. Brack. 2012. Capture and reproductive trends in

summer bat communities in West Virginia: assessing the impact of White-Nose

Syndrome. Journal of Fish and Wildlife Management 3:33–42.

Frick, W. F., S. J. Puechmaille, and C. K. R. Willis. 2016. White-nose syndrome in bats. Pages

245–262 in. Bats in the Anthropocene: Conservation of Bats in a Changing World.

Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-25220-9_9.

Accessed 25 Jul 2017.

Garroway, C. J., and H. G. Broders. 2007. Nonrandom association patterns at northern long-

eared bat maternity roosts. Canadian Journal of Zoology 85:956–964.

Garroway, C. J., and H. G. Broders. 2008. Day roost characteristics of northern long-eared bats

(Myotis septentrionalis) in relation to female reproductive status. Ecoscience 15:89–93.

Goodburn, J. M., and C. G. Lorimer. 1998. Cavity trees and coarse woody debris in old-growth

and managed northern hardwood forests in Wisconsin and Michigan. Canadian Journal of

Forest Research 28:427–438.

Hamilton, I. M., and R. M. R. Barclay. 1994. Patterns of daily torpor and day-roost selection by

male and female big brown bats (Eptesicus fuscus). Canadian Journal of Zoology

72:744–749.

Henderson, L. E., and H. G. Broders. 2008. Movements and resource selection of the northern

long-eared myotis (Myotis septentrionalis) in a forest--agriculture landscape. Journal of

Mammalogy 89:952–963.

159

Johnson, J. B., J. W. Edwards, and W. M. Ford. 2011. Nocturnal Activity Patterns of Northern

Myotis (Myotis septentrionalis ) during the Maternity Season in West Virginia (USA).

Acta Chiropterologica 13:391–397.

Johnson, J. B., J. W. Edwards, W. M. Ford, and J. E. Gates. 2009. Roost tree selection by

northern myotis (Myotis septentrionalis) maternity colonies following prescribed fire in a

Central Appalachian Mountains hardwood forest. Forest Ecology and Management

258:233–242.

Johnson, J. B., W. M. Ford, and J. W. Edwards. 2012. Roost networks of northern myotis

(Myotis septentrionalis) in a managed landscape. Forest Ecology and Management

266:223–231.

Johnson, J. S., J. N. Kropczynski, and M. J. Lacki. 2013. Social network analysis and the study

of sociality in bats. Acta Chiropterologica 15:1–17.

Kerth, G., M. Wagner, and B. König. 2001. Roosting together, foraging apart: information

transfer about food is unlikely to explain sociality in female Bechstein’s bats (Myotis

bechsteinii). Behavioral Ecology and Sociobiology 50:283–291.

Lacki, M. J., D. R. Cox, L. E. Dodd, and M. B. Dickinson. 2009. Response of northern bats

(Myotis septentrionalis) to prescribed fires in eastern Kentucky forests. Journal of

Mammalogy 90:1165–1175.

Lacki, M. J., and J. H. Schwierjohann. 2001. Day-roost characteristics of northern bats in mixed

mesophytic forest. The Journal of Wildlife Management 65:482–488.

Langwig, K. E., W. F. Frick, J. T. Bried, A. C. Hicks, T. H. Kunz, and A. Marm Kilpatrick.

2012. Sociality, density-dependence and microclimates determine the persistence of

populations suffering from a novel fungal disease, white-nose syndrome. Ecology Letters

15:1050–1057.

Lourenço, S. I., and J. M. Palmeirim. 2004. Influence of temperature in roost selection by

Pipistrellus pygmaeus (Chiroptera): relevance for the design of bat boxes. Biological


Menzel, M. A., J. M. Menzel, S. B. Castleberry, J. Ozier, W. M. Ford, and J. W. Edwards.

2002a. Illustrated key to skins and skulls of bats in the southeastern and mid-Atlantic

states. USDA Forest Service, Research Note NE-376, Newton Square, PA.

Menzel, M. A., S. F. Owen, W. M. Ford, J. W. Edwards, P. B. Wood, B. R. Chapman, and K. V.

Miller. 2002b. Roost tree selection by northern long-eared bat (Myotis septentrionalis)

maternity colonies in an industrial forest of the central Appalachian Mountains. Forest

Ecology and Management 155:107–114.

Mitchell, K. 2010. Quantitative analysis by the point-centered quarter method. Hobart and

William Smith Colleges. Available at: http://arxiv.org/pdf/1010.3303.pdf

160

Nowacki, G. J., and M. D. Abrams. 2008. The demise of fire and “mesophication” of forests in

the eastern United States. BioScience 58:123–138.

Nyland, R. D. 2002. Silviculture: concepts and applications. Second edition. McGraw-Hill, New

York, NY.

Owen, S. F., M. A. Menzel, W. M. Ford, B. R. Chapman, K. V. Miller, J. W. Edwards, and P. B.

Wood. 2003. Home-range size and habitat used by the northern Myotis (Myotis

septentrionalis). American Midland Naturalist 150:352–359.

Owen, S. F., M. A. Menzel, W. M. Ford, J. W. Edwards, B. R. Chapman, K. V. Miller, and P. B.

Wood. 2002. Roost tree selection by maternal colonies of northern long-eared myotis in

an intensively managed forest. Gen. Tech. Rep. NE-292. Newtown Square, PA: U. S.

Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 6 p.

Patriquin, K. J., M. L. Leonard, H. G. Broders, W. M. Ford, E. R. Britzke, and A. Silvis. 2016.

Weather as a proximate explanation for fission–fusion dynamics in female northern long-

eared bats. Animal Behaviour 122:4757.

Patriquin, K. J., F. Palstra, M. L. Leonard, and H. G. Broders. 2013. Female northern myotis

(Myotis septentrionalis) that roost together are related. Behavioral Ecology 24:949–954.

Perry, R. W. 2011. Fidelity of bats to forest sites revealed from mist-netting recaptures. Journal

of Fish and Wildlife Management 2:112–116.

Perry, R. W., and R. E. Thill. 2007. Roost selection by male and female northern long-eared bats

in a pine-dominated landscape. Forest Ecology and Management 247:220–226.

Pettit, J. L., and J. M. O’Keefe. 2017. Impacts of white-nose syndrome observed during long-

term monitoring of a midwestern bat community. Journal of Fish and Wildlife

Management 8:69–78.

R. Development Core Team. 2014. R: A language and environment for statistical computing.

Vienna, Austria. http://www.R-project.org/.

Reynolds, R. J., K. E. Powers, W. Orndorff, W. M. Ford, and C. S. Hobson. 2016. Changes in

rates of capture and demographics of Myotis septentrionalis (Northern Long-eared Bat)

in Western Virginia before and after onset of White-nose Syndrome. Northeastern


Sasse, B., and P. Pekins. 1996. Summer roosting ecology of northern long-eared bats (Myotis

septentrionalis) in the White Mountain National Forest. In: Barclay, R.M.R.; Brigham, R.

M. eds. Bats and forest symposium, Working Paper 23/1996. Victoria, BC: British

Columbia Ministry of Forests. 91-101.

Silvis, A. 2014. Day-roosting social ecology of the northern long-eared bat (Myotis

septentrionalis) and the endangered Indiana Bat (Myotis sodalis). Dissertation, Virgnia

161

Polytechnic Institute and State University, Blacksburg, VA, USA.

http://vtechworks.lib.vt.edu/handle/10919/51088.

Silvis, A., N. Abaid, W. M. Ford, and E. R. Britzke. 2016a. Responses of bat social groups to

roost loss: more questions than answers. Pages 261–280 in Sociality in Bats. Springer

International Publishing.

Silvis, A., W. M. Ford, and E. R. Britzke. 2015a. Day-roost tree selection by northern long-eared

bats—What do non-roost tree comparisons and one year of data really tell us? Global

Ecology and Conservation 3:756–763.

Silvis, A., W. M. Ford, and E. R. Britzke. 2015b. Effects of hierarchical roost removal on

northern long-eared bat (Myotis septentrionalis) maternity colonies. PLoS ONE 10(1):

e0116356. https://doi.org/10.1371/journal.pone.0116356

Silvis, A., W. M. Ford, E. R. Britzke, N. R. Beane, and J. B. Johnson. 2012. Forest succession

and maternity day roost selection by Myotis septentrionalis in a mesophytic hardwood

forest. International Journal of Forestry Research 2012:8. Article ID 148106.

Silvis, A., W. M. Ford, E. R. Britzke, and J. B. Johnson. 2014. Association, roost use and

simulated disruption of Myotis septentrionalis maternity colonies. Behavioural Processes

103:283–290.

Silvis, A., R. Perry, and W. M. Ford. 2016b. Relationships of three species of bats impacted by

white-nose syndrome to forest condition and management.

http://www.treesearch.fs.fed.us/pubs/52250. Accessed 23 Sep 2016.

Silvis, A., R. E. Thomas, W. M. Ford, E. R. Britzke, and M. J. Friedrich. 2015c. Internal cavity

characteristics of northern long-eared bat (Myotis septentrionalis) maternity day-roosts.

United States Department of Agriculture, Forest Service, Northern Research Station.

Stein, R. M., and J. A. White. 2016. Maternity colony of northern long-eared myotis (Myotis

septentrionalis) in a human-made structure in Nebraska. Transactions of the Nebraska

Academy of Sciences, 36:1-5.

USFWS. 2017. Final 4(d) Rule - Questions and answers.

https://www.fws.gov/midwest/endangered/mammals/nleb/FAQsFinal4dRuleNLEB.html.

Accessed 23 Feb 2018.

Willis, C., and R. Brigham. 2007. Social thermoregulation exerts more influence than

microclimate on forest roost preferences by a cavity-dwelling bat. Behavioral Ecology

and Sociobiology 62:97–108.

Willis, C. K. R., C. M. Voss, and R. M. Brigham. 2006. Roost selection by forest-living female

big brown bats (Eptesicus fuscus). Journal of Mammalogy 87:345–350.

162

Tables

Table 3-1: Female northern long-eared bat (Myotis septentrionalis) day roosts by tree species,

number found, percentage of total day roosts, roost days (number of days used) by radio-tagged

bats, and percentage of total roost days by radio-tagged bats in deciduous hardwood forest on

George Washington and Jefferson National Forest and adjacent private lands in Bath County,

Virginia, 2015 and 2016.

Tree Species Day roosts

% Total

day roosts

# Roost

days

% Total

roost days

Red Maple (Acer rubrum) 5 33 13 33

Chestnut oak (Quercus montana) 4 27 6 15

Sassafras (Sassafras albidum) 2 13 2 5

Blackgum (Nyssa sylvatica) 2 13 14 35

Hickory (Carya spp.) 1 7 4 10

Black locust (Robinia pseudoacacia) 1 7 1 3

163

Table 3-2: Tree species selected as day-roosts and % total uses by female northern long-eared bat (Myotis septentrionalis) maternity

colonies on George Washington and Jefferson National Forest and adjacent private lands in Bath County, Virginia, 2015 and 2016

(following White-nose Syndrome, Post-WNS); randomly selected trees within landscape surrounding Post-WNS day-roosts; and tree

species selected as day-roosts and % total uses by female northern long-eared bat (Myotis septentrionalis) maternity colonies on the

Fernow Experimental Forest, Tucker County, Virginia, 2007 and 2008 (prior to White-nose Syndrome, Pre-WNS).

Tree species

Post-WNS

day roosts

% Total

Post-WNS

day roosts

Random

trees

% Total

random trees

Pre-WNS day

roosts

% Total

Pre-WNS

day roosts

Acer rubrum 5 33 0 0 10 14.3

Quercus spp. 4 27 11 36.7 10 14.3

Nyssa slyvatica 2 13 7 23.3 0 0.0

Sassafras albidum 2 13 1 3.3 5 7.1

Carya spp. 1 7 1 3.3 1 1.4

Robinia pseudoacacia 1 7 2 6.7 34 48.6

Liriodendron tulipifera 0 0 1 3.3 1 1.4

Prunus serotina 0 0 0 0.0 3 4.3

Magnolia spp. 0 0 0 0.0 1 1.4

Oxydendrum arboreum 0 0 0 0.0 3 4.3

Unknown 0 0 0 0.0 2 2.9

Other 0 0 7 23.3 0 0.0

164

Table 3-3: Mean ± SD values of female northern long-eared bat (Myotis septentrionalis) day roost characteristics and percent of trees

in each crown class for Post-WNS day roost trees located on George Washington and Jefferson Forest and private lands (Bath County,

VA) and randomly selected trees located within and on the surrounding landscape. An asterisk (*) indicates a significant difference (P

< 0.05) between characteristics of day roosts located post-WNS and those of randomly selected trees.

Post-WNS day roosts Random Trees

N 15 30

dbh (cm) 16.33 ± 7.12* 18.32 ± 12.96*

Tree height (m) 10.26 ± 6.44* 11.12 ± 6.13

Canopy closure (%) 87.99 ± 14.14* 91.98 ± 17.59

Bark remaining (%) 60.87 ± 33.08* 90.90 ± 20.74*

Surrounding basal area m²/ha 32.75 ± 16.08* 28.62 ± 9.00*

Dead/declining (% of N) 73.3* 33.3*

Dominant crown class (% of N) 40.0* 26.7

Codominant crown class (% of N) 20.0* 16.7

Intermediate crown class (% of N) 40.0* 43.3

Suppressed crown class (% of N) 0.0* 13.3

165

Table 3-4: Mean ± SD values of female northern long-eared bat (Myotis septentrionalis) day roost characteristics and percent of trees

in each crown class for pre-White-nose Syndrome (WNS) day roost trees located on the Fernow Experimental Forest (Tucker County,

West Virginia) and Post-WNS day roost trees located on George Washington and Jefferson Forest and private lands (Bath County,

Virginia). An asterisk (*) indicates a significant difference (P < 0.05) between characteristics of day roosts located post-WNS and

those of day roosts located pre-WNS.

Pre-WNS day

roosts

Post-WNS day

roosts

N 69 15

dbh (cm) 28.53 ± 17.22* 16.33 ± 7.12*

Tree height (m) 15.45 ± 7.46* 10.26 ± 6.44*

Roost height (m) 8.69 ± 4.51* 5.37 ± 3.64*

Cavity roost (% of N) 84.1 86.7

Canopy closure (%) 80.02 ± 19.32* 87.99 ± 14.14*

Bark remaining (%) 69.40 ± 40.70 60.87 ± 33.08*

Surrounding Basal Area m²/ha 34.20 ± 10.40 32.75 ± 16.08*

Dead/Declining (% of N) 81.2 73.3*

Dominant crown class (% of N) 4.4* 40.0*

Codominant crown class (% of N) 39.1* 20.0*

Intermediate crown class (% of N) 33.3* 40.0*

Suppressed crown class (% of N) 23.2* 0.0*

166

Figures

Figure 3-1: Locations of female northern long-eared bat (Myotis septentrionalis) day roosts

found in Bath County, Virginia, during summer 2015 and 2016. The minimum convex polygon

(MCP) for 2015 roosts encompassed 20.8 hectares, and the MCP for the 2016 roosts

encompassed 39.8 hectares.

Seasonal Activity Patterns of Bats in the Central …...around three caves in the central...

Documents

Transcript of Seasonal Activity Patterns of Bats in the Central …...around three caves in the central...